importing¶

In [1]:
#@title drive
# from google.colab import drive
# drive.mount('/content/drive')
In [2]:
#@title pandas and read specific excel
import pandas as pd
df = pd.read_excel('0_8001-8500.xlsx', sheet_name='Sheet1')
In [3]:
#@title warning
import warnings
warnings.filterwarnings("ignore")

first look¶

In [4]:
#@title set option to display max
pd.set_option('display.max_columns', None)
# pd.set_option('display.max_rows', None)
In [5]:
#@title display sample original dataset
display(df.sample(4))
S. NO. REGISTRATION DATE INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT
106 8107.0 2076-08-10 ASIANINTERNATIONALREGENCYPVT.LTD. RUPANDEHI 2380000000 2340000000 40000000 HOTEL176BEDSRESTAURANT225SEATS 97 TOURISM LARGE 2000\nKVA Local100%
159 8160.0 2076-09-22 SHUNYUANINTERNATIONALCARGOPVT.LTD. KATHMANDU 150000000 145000000 5000000 INTERNATIONALCARGOHANDLING16000MT 63 SERVICE MEDIUM 10KVA Foreign100%
251 8252.0 2077-03-31 MONA\nHYDROPOWERLIMITED MYAGDI 997693380 969371270 28322110 HYDROELECTRICITY5.5M.W. 39 ENERGY LARGE 50\nK.V.A. Local100%
28 8029.0 2076-05-20 CHENXINGRESTAURANTPVT.LTD. KATHMANDU 150000000 110000000 40000000 RESTAURANT200SEATS 25 TOURISM MEDIUM 25KVA Foreign100%
In [6]:
#@title drop empty rows
df.dropna(how='all', inplace=True)
In [7]:
# @title shape
df.shape
Out[7]:
(500, 13)
In [8]:
#@title query to specific
df.query('`S. NO.` == 8377')
Out[8]:
S. NO. REGISTRATION DATE INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT
376 8377.0 2077-11-11 SEPLIHYDROPOWERDEVELOPMENTCOMPANYPVT.LTD. OKHALDHUNGA 890136907 881500000 8636907 HYDROELECTRICITY5M.W. 11 ENERGY LARGE 60\nK.V.A. Local100%
In [9]:
#@title copy df
df1 = df.copy(deep=True)
In [10]:
#@title info
df1.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 500 entries, 0 to 499
Data columns (total 13 columns):
 #   Column                       Non-Null Count  Dtype         
---  ------                       --------------  -----         
 0   S. NO.                       500 non-null    float64       
 1   REGISTRATION DATE            500 non-null    datetime64[ns]
 2   INDUSTRY NAME                500 non-null    object        
 3   DISTRICT                     486 non-null    object        
 4   TOTAL CAPITAL                500 non-null    int64         
 5   FIXED CAPITAL                500 non-null    int64         
 6   WORKING CAPITAL              500 non-null    int64         
 7   PRODUCT AND ANNUAL CAPACITY  500 non-null    object        
 8   EMPLOYMENT                   500 non-null    int64         
 9   CATEGORY                     500 non-null    object        
 10  SCALE                        500 non-null    object        
 11  POWER                        500 non-null    object        
 12  % OF INVESTMENT              500 non-null    object        
dtypes: datetime64[ns](1), float64(1), int64(4), object(7)
memory usage: 50.9+ KB
In [11]:
#@title display non available
display(df1.isna().sum())
S. NO.                          0
REGISTRATION DATE               0
INDUSTRY NAME                   0
DISTRICT                       14
TOTAL CAPITAL                   0
FIXED CAPITAL                   0
WORKING CAPITAL                 0
PRODUCT AND ANNUAL CAPACITY     0
EMPLOYMENT                      0
CATEGORY                        0
SCALE                           0
POWER                           0
% OF INVESTMENT                 0
dtype: int64
In [12]:
#@title drop it
# df1 = df1.dropna()
In [13]:
#@title describe df1
display(df1.describe())
S. NO. REGISTRATION DATE TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL EMPLOYMENT
count 500.000000 500 5.000000e+02 5.000000e+02 5.000000e+02 500.000000
mean 8250.500000 2076-11-05 13:14:52.800000 6.454023e+08 5.805375e+08 6.486473e+07 63.918000
min 8001.000000 1978-03-01 00:00:00 2.135500e+06 8.855000e+05 9.000000e+05 0.000000
25% 8125.750000 2076-08-18 00:00:00 1.100000e+08 9.100000e+07 9.419560e+06 30.000000
50% 8250.500000 2077-03-28 12:00:00 2.000000e+08 1.513500e+08 2.585000e+07 45.000000
75% 8375.250000 2077-11-03 18:00:00 3.676000e+08 2.565223e+08 6.212541e+07 70.000000
max 8500.000000 2078-05-18 00:00:00 1.762406e+10 1.757100e+10 2.471689e+09 550.000000
std 144.481833 NaN 1.526715e+09 1.494585e+09 1.659136e+08 65.985236
In [14]:
#@title registration data info and describe
display(df1['REGISTRATION DATE'].info())
display(df1['REGISTRATION DATE'].describe())
<class 'pandas.core.series.Series'>
RangeIndex: 500 entries, 0 to 499
Series name: REGISTRATION DATE
Non-Null Count  Dtype         
--------------  -----         
500 non-null    datetime64[ns]
dtypes: datetime64[ns](1)
memory usage: 4.0 KB
None
count                           500
mean     2076-11-05 13:14:52.800000
min             1978-03-01 00:00:00
25%             2076-08-18 00:00:00
50%             2077-03-28 12:00:00
75%             2077-11-03 18:00:00
max             2078-05-18 00:00:00
Name: REGISTRATION DATE, dtype: object
In [15]:
#@title scale info and value count
display(df1['SCALE'].info())
display(df1['SCALE'].value_counts())
<class 'pandas.core.series.Series'>
RangeIndex: 500 entries, 0 to 499
Series name: SCALE
Non-Null Count  Dtype 
--------------  ----- 
500 non-null    object
dtypes: object(1)
memory usage: 4.0+ KB
None
SCALE
SMALL     216
MEDIUM    183
LARGE     101
Name: count, dtype: int64
In [16]:
#@title category info and value count
display(df1['CATEGORY'].info())
display(df1['CATEGORY'].value_counts())
<class 'pandas.core.series.Series'>
RangeIndex: 500 entries, 0 to 499
Series name: CATEGORY
Non-Null Count  Dtype 
--------------  ----- 
500 non-null    object
dtypes: object(1)
memory usage: 4.0+ KB
None
CATEGORY
MANUFACTURING             155
TOURISM                   129
SERVICE                    83
ENERGY                     72
INFORMATION TECHNOLOGY     25
AGR0AND\nFORESTRY          20
AGRO AND FORESTRY          13
INFRASTRUCTURE              2
MINERAL                     1
Name: count, dtype: int64
In [17]:
#@title dsitrict info and value counts
print(df1['DISTRICT'].info())
display(df1['DISTRICT'].value_counts())
print(df1['DISTRICT'].nunique())
<class 'pandas.core.series.Series'>
RangeIndex: 500 entries, 0 to 499
Series name: DISTRICT
Non-Null Count  Dtype 
--------------  ----- 
486 non-null    object
dtypes: object(1)
memory usage: 4.0+ KB
None
DISTRICT
KATHMANDU         157
LALITPUR           37
KASKI              25
RUPANDEHI          21
BARA               19
NAWALPARASI        19
MORANG             17
CHITWAN            15
BHAKTAPUR          12
SUNSARI            12
JHAPA              11
PARSA              10
SINDHUPALCHOWK      9
DHADING             8
KAILALI             8
GORKHA              7
NUWAKOT             7
KAPILBASTU          7
MAKWANPUR           7
DOLKHA              6
SANKHUWASABHA       6
SOLUKHUMBU          6
MYAGDI              6
BANKE               6
KAVRE               6
LAMJUNG             5
DHANUSHA            5
TAPLEJUNG           4
ILAM                3
RASUWA              2
MAHOTTARI           2
DANG                2
OKHALDHUNGA         2
MANANG              2
SARLAHI             2
RAUTAHAT            2
KHOTANG             2
TANAHU              2
BARDIYA             1
BAJURA              1
RUKUM               1
BAGLUNG             1
SIRAHA              1
SINDHULI            1
KANCHANPUR          1
Name: count, dtype: int64
45
In [18]:
#@title % of investment nunique and calue count
print(df1['% OF INVESTMENT'].nunique())
display(df1['% OF INVESTMENT'].value_counts())
26
% OF INVESTMENT
Local100%                         210
Foreign100%                       121
Foreign-100%                       75
Local-100%                         59
Local-40%\nForeign-60%              4
Local1000Zo                         4
Local-20%\nForeign-80%              3
Local-51%\nForeign-49%              3
Local-10%\nForeign-90%              2
Local-6.25%Foreign-93.75%           2
Local-15%\nForeign-85%              2
Local-50%\nForeign-50%              1
Local-36%\nForeign-64%              1
Local-5.67%Foreign-94.33%           1
Local-34.221%\nForeign-65.779%      1
Local-9.91%Foreign-90.090/o         1
Local50%\nForeign50%                1
Local-15%Foreign-85%                1
Local-13.04%Foreign-86.96%          1
Local1000/o                         1
Local-15.24%Foreign-84.76%          1
Local-49%\nForeign-51%              1
Local-16.667%\nForeign-83.333%      1
Local-20.29%Foreign-79.71%          1
Local-66.42%Foreign-33.58%          1
Local-6%\nForeign-94%               1
Name: count, dtype: int64
In [19]:
#@title power describe info
display(df1['POWER'].describe())
display(df1['POWER'].info())
count        500
unique        73
top       100KVA
freq          40
Name: POWER, dtype: object
<class 'pandas.core.series.Series'>
RangeIndex: 500 entries, 0 to 499
Series name: POWER
Non-Null Count  Dtype 
--------------  ----- 
500 non-null    object
dtypes: object(1)
memory usage: 4.0+ KB
None

clean¶

In [20]:
#@title copy to clean
df2=df1.copy(deep=True)
In [21]:
#@title check data type
df2.dtypes
Out[21]:
S. NO.                                float64
REGISTRATION DATE              datetime64[ns]
INDUSTRY NAME                          object
DISTRICT                               object
TOTAL CAPITAL                           int64
FIXED CAPITAL                           int64
WORKING CAPITAL                         int64
PRODUCT AND ANNUAL CAPACITY            object
EMPLOYMENT                              int64
CATEGORY                               object
SCALE                                  object
POWER                                  object
% OF INVESTMENT                        object
dtype: object
In [22]:
#@title drop SNo
df2 = df2.drop(['S. NO.'], axis=1)
In [23]:
#@title fix date column extract year and month

# convert  to a string
df2['REGISTRATION DATE'] = df2['REGISTRATION DATE'].astype(str)

# date part
df2['MONTH'] = df2['REGISTRATION DATE'].str[5:7].astype(int)

# year part
df2['YEAR'] = df2['REGISTRATION DATE'].str[:4]
df2['YEAR'] = pd.to_numeric(df2['YEAR'], errors='coerce')
df2['YEAR'] = df2['YEAR'].astype('Int64')

# drop original
df2 = df2.drop('REGISTRATION DATE', axis=1)
In [24]:
#@title regular expression
import re
In [25]:
#@title remove repeating liability of stakeholder and cleaninf

df2['INDUSTRY NAME'] = df2['INDUSTRY NAME'].replace(r'PVT\.|LTD\.|\n', '', regex=True) \
                                             .replace(r'\s+', ' ', regex=True) \
                                             .str.strip()
In [26]:
#@title check investment
print(df2['% OF INVESTMENT'].unique())
['Foreign-100%' 'Local100%' 'Local-100%' 'Local-6.25%Foreign-93.75%'
 'Foreign100%' 'Local-66.42%Foreign-33.58%' 'Local-51%\nForeign-49%'
 'Local-20.29%Foreign-79.71%' 'Local-16.667%\nForeign-83.333%'
 'Local-49%\nForeign-51%' 'Local-40%\nForeign-60%'
 'Local-10%\nForeign-90%' 'Local1000Zo' 'Local-15%Foreign-85%'
 'Local-15.24%Foreign-84.76%' 'Local1000/o' 'Local-20%\nForeign-80%'
 'Local-13.04%Foreign-86.96%' 'Local-36%\nForeign-64%'
 'Local-9.91%Foreign-90.090/o' 'Local-15%\nForeign-85%'
 'Local-5.67%Foreign-94.33%' 'Local50%\nForeign50%'
 'Local-50%\nForeign-50%' 'Local-34.221%\nForeign-65.779%'
 'Local-6%\nForeign-94%']
In [27]:
#@title turn % of investment to standard format
def clean_investment_percentage(value):
    value = str(value).replace('\n', ' ').replace('0Zo', ' ').replace('0/o', ' ').replace('/', ' ')
    value = re.sub(r'\s+', ' ', value).strip()
    local_match = re.search(r'Local\s*[-–—:\s]?\s*([\d.,]+)%', value)
    foreign_match = re.search(r'Foreign\s*[-–—:\s]?\s*([\d.,]+)%', value)

    local_pct = local_match.group(1).replace(',', '') if local_match else '0'
    foreign_pct = foreign_match.group(1).replace(',', '') if foreign_match else '0'

    return f"Local - {local_pct}%, Foreign - {foreign_pct}%"

df2['% OF INVESTMENT'] = df2['% OF INVESTMENT'].apply(clean_investment_percentage)
In [28]:
df2['% OF INVESTMENT'].value_counts()
Out[28]:
% OF INVESTMENT
Local - 100%, Foreign - 0%            269
Local - 0%, Foreign - 100%            196
Local - 0%, Foreign - 0%                5
Local - 40%, Foreign - 60%              4
Local - 15%, Foreign - 85%              3
Local - 51%, Foreign - 49%              3
Local - 20%, Foreign - 80%              3
Local - 50%, Foreign - 50%              2
Local - 10%, Foreign - 90%              2
Local - 6.25%, Foreign - 93.75%         2
Local - 66.42%, Foreign - 33.58%        1
Local - 36%, Foreign - 64%              1
Local - 34.221%, Foreign - 65.779%      1
Local - 5.67%, Foreign - 94.33%         1
Local - 9.91%, Foreign - 0%             1
Local - 15.24%, Foreign - 84.76%        1
Local - 13.04%, Foreign - 86.96%        1
Local - 49%, Foreign - 51%              1
Local - 16.667%, Foreign - 83.333%      1
Local - 20.29%, Foreign - 79.71%        1
Local - 6%, Foreign - 94%               1
Name: count, dtype: int64
In [29]:
#@title check for null, empty, nan
# Check for null values in '% OF INVESTMENT'
print(df2['% OF INVESTMENT'].isnull().sum())

# Check for empty strings in '% OF INVESTMENT'
print((df2['% OF INVESTMENT'] == '').sum())

# Check for NaN values in '% OF INVESTMENT'
print(df2['% OF INVESTMENT'].isna().sum())
0
0
0
In [30]:
#@title only keep numbers from power
df2['POWER'] = df2['POWER'].apply(lambda x: re.sub(r'\D', '', str(x)))
In [31]:
#@title clean power only keep numeric
df2['POWER'] = df2['POWER'].str.replace('\n', ' ', regex=False).str.replace(r'\s+', ' ', regex=True).str.replace('KVA', '', regex=False).str.strip().astype(int)
In [32]:
#@title clean category
df2['CATEGORY'] = df2['CATEGORY'].str.replace('AGR0AND\nFORESTRY', 'AGRO AND FORESTRY')
print(df2['CATEGORY'].unique())
['SERVICE' 'ENERGY' 'INFORMATION TECHNOLOGY' 'TOURISM' 'MANUFACTURING'
 'AGRO AND FORESTRY' 'MINERAL' 'INFRASTRUCTURE']
In [33]:
#@title display clean dataset
display(df2)
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR
0 HUALICONSTRUCTIONANDENGINEERING BHAKTAPUR 150000000 87000000 63000000 VARIOUSKINDSOFCONSTRUCTIONWORKS(CONSTRUCTIONRE... 88 SERVICE SMALL 10 Local - 0%, Foreign - 100% 4 2076
1 CHISANGHYDRO MORANG 304505000 296587693 7917307 Hydroelectricproduction1.8MW 31 ENERGY LARGE 30 Local - 100%, Foreign - 0% 4 2076
2 MOKSHAINTERNATIONALCARGO KATHMANDU 50000000 46000000 4000000 INTERNATIONALCARGOHANDLING12000MT 50 SERVICE SMALL 10 Local - 0%, Foreign - 100% 5 2076
3 TENGFEICONSTRUCTIONCOMPANY KATHMANDU 300000000 227000000 73000000 CONSTRUCTIONWORKSOFVARIOUSTYPE1000000000L.S 375 SERVICE MEDIUM 100 Local - 0%, Foreign - 100% 5 2076
4 S.W.SOFTWARE LALITPUR 250000000 227000000 23000000 SOFTWAREDEVELOPMENT350PACKAGE 83 INFORMATION TECHNOLOGY MEDIUM 25 Local - 0%, Foreign - 100% 5 2076
... ... ... ... ... ... ... ... ... ... ... ... ... ...
495 AGRIVASTUCOLDST0RAGE KAPILBASTU 234192207 160104320 74087887 COLDST0RAGE1800MT.PRODUCTIONPROCESSINGAND\nSTO... 27 AGRO AND FORESTRY MEDIUM 300 Local - 100%, Foreign - 0% 5 2078
496 GHORAHICEMENTINDUSTRYLIMITED(CEMENTPACKEGING) BANKE 210338607 167189200 43149407 CEMENT120000MT. 48 SERVICE MEDIUM 1000 Local - 100%, Foreign - 0% 5 2078
497 S.S.PRODUCTS SARLAHI 2135500 885500 1250000 PANMASALA(PLAINJARDA)14250KG. 22 MANUFACTURING SMALL 15 Local - 100%, Foreign - 0% 5 2078
498 SHIVASHAKTIOILANDFATS(OIL) BARA 500000000 300000000 200000000 REFINEDSOYABEANOIL30000MT.REFINEDSUNFLOWEROIL3... 60 MANUFACTURING MEDIUM 1000 Local - 100%, Foreign - 0% 5 2078
499 SHERPAOUTDOORSPORTSGOODSINDUSTRIES KATHMANDU 170000000 150000000 20000000 READYMADEGARMENT&TREKKINGGOODSSUCHASSLEEPINGBA... 200 MANUFACTURING SMALL 300 Local - 100%, Foreign - 0% 5 2078

500 rows × 13 columns

In [34]:
#@title save dataset
df2.to_csv('1_cleaned.csv', index=False)
In [35]:
#@title value count of scale
display(df2['SCALE'].value_counts())
SCALE
SMALL     216
MEDIUM    183
LARGE     101
Name: count, dtype: int64

feature engineering¶

In [36]:
#@title upload file to temporary colab runtime
# from google.colab import files
# uploaded = files.upload()
# for filename, content in uploaded.items():
#        with open(filename, 'wb') as f:
#            f.write(content)
In [37]:
#@title import clean csv
# import pandas as pd
df2 = pd.read_csv('1_cleaned.csv')
In [38]:
#@title view list of columns
columns = df2.columns.tolist()
print("Columns:", columns)
Columns: ['INDUSTRY NAME', 'DISTRICT', 'TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL', 'PRODUCT AND ANNUAL CAPACITY', 'EMPLOYMENT', 'CATEGORY', 'SCALE', 'POWER', '% OF INVESTMENT', 'MONTH', 'YEAR']
In [39]:
#@title display sample of dataset
for column in columns:
    print(f"\nColumn: {column}")
    display(df2[column].sample(4))
Column: INDUSTRY NAME
181     GURANSHERBACEUTICALS
21                ZEGALNEPAL
247    JALSHAKTIHYDROCOMPANY
76         RUIYONGRESTAURANT
Name: INDUSTRY NAME, dtype: object
Column: DISTRICT
14       SUNSARI
378    MAKWANPUR
216    KATHMANDU
243    KATHMANDU
Name: DISTRICT, dtype: object
Column: TOTAL CAPITAL
4       250000000
301     250000000
303    4079657696
199     833543925
Name: TOTAL CAPITAL, dtype: int64
Column: FIXED CAPITAL
266    145000000
376    881500000
92       4500000
380     26500000
Name: FIXED CAPITAL, dtype: int64
Column: WORKING CAPITAL
106    40000000
232    41475000
300    96600000
85      8000000
Name: WORKING CAPITAL, dtype: int64
Column: PRODUCT AND ANNUAL CAPACITY
272           INTERNATIONALCARGOHANDLINGSERVICE20000M.T.
304                             HYDROELECTRICITY22.9M.W.
382      LABELIMPRESSION(1.15MTOR75000SEATPERDAY)318M.T.
336    VATI/GUGGUL/CAPSULE/TABLET900000KG.SYRUP/ASVA\...
Name: PRODUCT AND ANNUAL CAPACITY, dtype: object
Column: EMPLOYMENT
385     39
346     94
290     68
75     100
Name: EMPLOYMENT, dtype: int64
Column: CATEGORY
355             MANUFACTURING
40              MANUFACTURING
174    INFORMATION TECHNOLOGY
2                     SERVICE
Name: CATEGORY, dtype: object
Column: SCALE
357     SMALL
349     SMALL
37     MEDIUM
153     SMALL
Name: SCALE, dtype: object
Column: POWER
4        25
322     100
110      50
484    1000
Name: POWER, dtype: int64
Column: % OF INVESTMENT
390    Local - 100%, Foreign - 0%
201    Local - 100%, Foreign - 0%
218    Local - 0%, Foreign - 100%
169    Local - 100%, Foreign - 0%
Name: % OF INVESTMENT, dtype: object
Column: MONTH
210    11
82      7
196    11
145     9
Name: MONTH, dtype: int64
Column: YEAR
490    2078
101    2076
408    2077
247    2077
Name: YEAR, dtype: int64
In [40]:
#@title random
import random
In [41]:
print(df2['DISTRICT'].nunique())
45
In [42]:
#@title check for district without value
display(df2[df2['DISTRICT'].isnull()])
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR
117 SPRINGHILLHOTEL NaN 50000000 47000000 3000000 HOTEL22BEDSRESTAURANT40SEATS 29 TOURISM SMALL 60 Local - 0%, Foreign - 100% 8 2076
249 HIMALAYANRENEWABLEOILINDUSTRY NaN 250000000 224492402 25507598 PETROL-1485KLDIESEL-1485KLLUBRICANTS(BYPRODUCT... 42 MANUFACTURING MEDIUM 2000 Local - 20%, Foreign - 80% 3 2077
250 NEBULAENERGY NaN 150000000 105000000 45000000 ELECTRICVEHICLESASSEMBLING300NOS. 67 MANUFACTURING SMALL 500 Local - 100%, Foreign - 0% 3 2077
256 PRISTINENEPALTERMINALS NaN 80000000 64000000 16000000 CONTAINERS14550MTBULK&BREAKBULK9050MT 74 SERVICE SMALL 100 Local - 36%, Foreign - 64% 4 2077
285 SAMADHIVILLAGE NaN 50000000 37500000 12500000 HOTEL20BEDSRESTAURANT100SEATS 17 TOURISM SMALL 50 Local - 0%, Foreign - 100% 6 2077
309 SETIKHOLAHYDROPWER NaN 5005648000 4945816000 59832000 HYDROELECTRICITY22M.W. 36 ENERGY LARGE 100 Local - 100%, Foreign - 0% 6 2077
342 PATHIVARAMATAFERTILIZERINDUSTRIES NaN 219800000 162400000 57400000 CHEMICALFERTILIZER3000M.T. 45 MANUFACTURING MEDIUM 900 Local - 100%, Foreign - 0% 9 2077
345 SWARARETREAT NaN 53000000 41500000 11500000 HOTEL40BEDSRESTAURANT60SEATS 38 TOURISM SMALL 50 Local - 5.67%, Foreign - 94.33% 9 2077
351 BOLBOMFEEDINDUSTRIES NaN 340000000 213000000 127000000 ANIMALFEED(PELLET/DISC)73000M.T. 55 AGRO AND FORESTRY MEDIUM 1000 Local - 100%, Foreign - 0% 9 2077
432 MANDAKINIHYDROPOWERCOMPANYLIMITED-1 NaN 569981000 565307297 4673703 HYDROELECTRICITY2.9M.W. 40 ENERGY LARGE 20 Local - 100%, Foreign - 0% 1 2078
434 ADIRATEXTILEINDUSTRIES NaN 200000000 137100000 62900000 VARIOUSFABRICS4200000SQ.M 59 MANUFACTURING SMALL 800 Local - 100%, Foreign - 0% 1 2078
435 ADIRAFLOORS NaN 150000000 114000000 36000000 PVCFLO0RINGSHEET1400MT. 46 MANUFACTURING SMALL 800 Local - 100%, Foreign - 0% 1 2078
492 HIMALI HYDROFUND NaN 1900000000 1850000000 50000000 HYDROELECTRICITY$MW 31 ENERGY LARGE 25 Local - 100%, Foreign - 0% 4 2078
494 SHIVAMVAGOILLTD NaN 750000000 400000000 350000000 VANASPATIGHEEANDREFINED0IL(SOYABEANSUNFLOWERRB... 72 MANUFACTURING MEDIUM 1500 Local - 100%, Foreign - 0% 5 2078
In [43]:
# @title list of districts to randomly impute
districts_to_impute = ['DOLPA', 'MUGU', 'HUMLA',
                       'JUMLA', 'SALYAN', 'JAJARKOT',
                       'DAILEKH', 'SURKHET', 'KALIKOT']
In [44]:
# @title find rows where 'DISTRICT' is NaN
rows_to_impute = df2[(df2['DISTRICT'].isna())]
In [45]:
# @title randomly impute districts to the identified rows
for index in rows_to_impute.index:
    df2.loc[index, 'DISTRICT'] = random.choice(districts_to_impute)
In [46]:
#@title district and region dictionary
province_district = {
    'KOSHI': ['TAPLEJUNG', 'SANKHUWASABHA', 'SOLUKHUMBU',
            'UDAYAPUR', 'PANCHTHAR', 'ILAM', 'TERHATHUM',
            'DHANKUTA', 'BHOJPUR', 'KHOTANG',
            'OKHALDHUNGA', 'JHAPA', 'MORANG', 'SUNSARI'],
    'MADHESH': ['MAHOTTARI', 'RAUTAHAT', 'DHANUSHA', 'SIRAHA',
                'BARA', 'SARLAHI', 'PARSA', 'SAPTARI'],
    'BAGMATI': ['DOLKHA', 'SINDHUPALCHOWK', 'RASUWA',
                'MAKWANPUR', 'BHAKTAPUR', 'LALITPUR',
                'KATHMANDU', 'NUWAKOT','RAMECHHAP', 'KAVRE',
                'DHADING', 'SINDHULI', 'CHITWAN'],
    'GANDAKI': ['MANANG', 'MUSTANG', 'PARBAT', 'SYANGJA','TANAHU', 
                'LAMJUNG', 'BAGLUNG', 'KASKI','MYAGDI', 'GORKHA', 'NAWALPARASI'],
    'LUMBINI': ['PALPA', 'ARGHAKHACHI', 'RUKUM', 'GULMI', 'PYUTHAN', 
                'ROLPA', 'RUPANDEHI', 'KAPILBASTU','DANG', 'BANKE', 'BARDIYA'],
    'KARNALI': ['DOLPA', 'MUGU', 'HUMLA', 'JUMLA', 'SALYAN', 
                'JAJARKOT', 'DAILEKH', 'SURKHET', 'KALIKOT'],
    'SUDUR-PASCHIM': ['BAJURA', 'BAJHANG', 'DARCHULA', 
                    'ACHHAM', 'DOTI', 'BAITADI', 'KAILALI', 'KANCHANPUR']
}
In [47]:
#@title apply function to map district to province
def map_district_to_province(district):
    for province, districts in province_district.items():
        if district in districts:
            return province
    return None

df2['PROVINCE'] = df2['DISTRICT'].apply(map_district_to_province)
In [48]:
#@title list null
print(df2[df2['PROVINCE'].isnull()]['DISTRICT'].unique().tolist())
[]
In [49]:
#@title check for impute
display(df2.query('PROVINCE == "KARNALI"'))
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE
117 SPRINGHILLHOTEL HUMLA 50000000 47000000 3000000 HOTEL22BEDSRESTAURANT40SEATS 29 TOURISM SMALL 60 Local - 0%, Foreign - 100% 8 2076 KARNALI
249 HIMALAYANRENEWABLEOILINDUSTRY DOLPA 250000000 224492402 25507598 PETROL-1485KLDIESEL-1485KLLUBRICANTS(BYPRODUCT... 42 MANUFACTURING MEDIUM 2000 Local - 20%, Foreign - 80% 3 2077 KARNALI
250 NEBULAENERGY HUMLA 150000000 105000000 45000000 ELECTRICVEHICLESASSEMBLING300NOS. 67 MANUFACTURING SMALL 500 Local - 100%, Foreign - 0% 3 2077 KARNALI
256 PRISTINENEPALTERMINALS KALIKOT 80000000 64000000 16000000 CONTAINERS14550MTBULK&BREAKBULK9050MT 74 SERVICE SMALL 100 Local - 36%, Foreign - 64% 4 2077 KARNALI
285 SAMADHIVILLAGE SALYAN 50000000 37500000 12500000 HOTEL20BEDSRESTAURANT100SEATS 17 TOURISM SMALL 50 Local - 0%, Foreign - 100% 6 2077 KARNALI
309 SETIKHOLAHYDROPWER KALIKOT 5005648000 4945816000 59832000 HYDROELECTRICITY22M.W. 36 ENERGY LARGE 100 Local - 100%, Foreign - 0% 6 2077 KARNALI
342 PATHIVARAMATAFERTILIZERINDUSTRIES JAJARKOT 219800000 162400000 57400000 CHEMICALFERTILIZER3000M.T. 45 MANUFACTURING MEDIUM 900 Local - 100%, Foreign - 0% 9 2077 KARNALI
345 SWARARETREAT MUGU 53000000 41500000 11500000 HOTEL40BEDSRESTAURANT60SEATS 38 TOURISM SMALL 50 Local - 5.67%, Foreign - 94.33% 9 2077 KARNALI
351 BOLBOMFEEDINDUSTRIES KALIKOT 340000000 213000000 127000000 ANIMALFEED(PELLET/DISC)73000M.T. 55 AGRO AND FORESTRY MEDIUM 1000 Local - 100%, Foreign - 0% 9 2077 KARNALI
432 MANDAKINIHYDROPOWERCOMPANYLIMITED-1 JAJARKOT 569981000 565307297 4673703 HYDROELECTRICITY2.9M.W. 40 ENERGY LARGE 20 Local - 100%, Foreign - 0% 1 2078 KARNALI
434 ADIRATEXTILEINDUSTRIES KALIKOT 200000000 137100000 62900000 VARIOUSFABRICS4200000SQ.M 59 MANUFACTURING SMALL 800 Local - 100%, Foreign - 0% 1 2078 KARNALI
435 ADIRAFLOORS JAJARKOT 150000000 114000000 36000000 PVCFLO0RINGSHEET1400MT. 46 MANUFACTURING SMALL 800 Local - 100%, Foreign - 0% 1 2078 KARNALI
492 HIMALI HYDROFUND KALIKOT 1900000000 1850000000 50000000 HYDROELECTRICITY$MW 31 ENERGY LARGE 25 Local - 100%, Foreign - 0% 4 2078 KARNALI
494 SHIVAMVAGOILLTD JAJARKOT 750000000 400000000 350000000 VANASPATIGHEEANDREFINED0IL(SOYABEANSUNFLOWERRB... 72 MANUFACTURING MEDIUM 1500 Local - 100%, Foreign - 0% 5 2078 KARNALI
In [50]:
#@title make a copy
df3 = df2.copy(deep=True)
In [51]:
#@title save to encode
df3.to_csv('2_features.csv', index=False)

encoding and outlier¶

In [52]:
#@title apply frequency encoding to category
district_counts = df3['CATEGORY'].value_counts(normalize=True)
df3['CATEGORY_FREQ'] = df3['CATEGORY'].map(district_counts)
display(df3[['CATEGORY','CATEGORY_FREQ']].value_counts())
CATEGORY                CATEGORY_FREQ
MANUFACTURING           0.310            155
TOURISM                 0.258            129
SERVICE                 0.166             83
ENERGY                  0.144             72
AGRO AND FORESTRY       0.066             33
INFORMATION TECHNOLOGY  0.050             25
INFRASTRUCTURE          0.004              2
MINERAL                 0.002              1
Name: count, dtype: int64
In [53]:
#@title reduceing district to province
display(df3['PROVINCE'].value_counts())
PROVINCE
BAGMATI          267
GANDAKI           67
KOSHI             63
MADHESH           41
LUMBINI           38
KARNALI           14
SUDUR-PASCHIM     10
Name: count, dtype: int64
In [54]:
#@title import label encoding for scale

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()
df3['SCALE_encode'] = le.fit_transform(df3['SCALE'])
df3['SCALE_encode'] = df3['SCALE_encode'] + 1

display(df3[['SCALE','SCALE_encode']].value_counts())
SCALE   SCALE_encode
SMALL   3               216
MEDIUM  2               183
LARGE   1               101
Name: count, dtype: int64
In [55]:
#@title import one hot encoding for province
from sklearn.preprocessing import OneHotEncoder

ohe = OneHotEncoder(sparse_output=False, handle_unknown='ignore')

ohe.fit(df3[['PROVINCE']])

encoded_data = ohe.transform(df3[['PROVINCE']])

feature_names = ohe.get_feature_names_out(['PROVINCE'])
for i, feature_name in enumerate(feature_names):
    df3[feature_name] = encoded_data[:, i]
In [56]:
#@title display encoded dataframe
display(df3)
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
0 HUALICONSTRUCTIONANDENGINEERING BHAKTAPUR 150000000 87000000 63000000 VARIOUSKINDSOFCONSTRUCTIONWORKS(CONSTRUCTIONRE... 88 SERVICE SMALL 10 Local - 0%, Foreign - 100% 4 2076 BAGMATI 0.166 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
1 CHISANGHYDRO MORANG 304505000 296587693 7917307 Hydroelectricproduction1.8MW 31 ENERGY LARGE 30 Local - 100%, Foreign - 0% 4 2076 KOSHI 0.144 1 0.0 0.0 0.0 1.0 0.0 0.0 0.0
2 MOKSHAINTERNATIONALCARGO KATHMANDU 50000000 46000000 4000000 INTERNATIONALCARGOHANDLING12000MT 50 SERVICE SMALL 10 Local - 0%, Foreign - 100% 5 2076 BAGMATI 0.166 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
3 TENGFEICONSTRUCTIONCOMPANY KATHMANDU 300000000 227000000 73000000 CONSTRUCTIONWORKSOFVARIOUSTYPE1000000000L.S 375 SERVICE MEDIUM 100 Local - 0%, Foreign - 100% 5 2076 BAGMATI 0.166 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
4 S.W.SOFTWARE LALITPUR 250000000 227000000 23000000 SOFTWAREDEVELOPMENT350PACKAGE 83 INFORMATION TECHNOLOGY MEDIUM 25 Local - 0%, Foreign - 100% 5 2076 BAGMATI 0.050 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
495 AGRIVASTUCOLDST0RAGE KAPILBASTU 234192207 160104320 74087887 COLDST0RAGE1800MT.PRODUCTIONPROCESSINGAND\nSTO... 27 AGRO AND FORESTRY MEDIUM 300 Local - 100%, Foreign - 0% 5 2078 LUMBINI 0.066 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0
496 GHORAHICEMENTINDUSTRYLIMITED(CEMENTPACKEGING) BANKE 210338607 167189200 43149407 CEMENT120000MT. 48 SERVICE MEDIUM 1000 Local - 100%, Foreign - 0% 5 2078 LUMBINI 0.166 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0
497 S.S.PRODUCTS SARLAHI 2135500 885500 1250000 PANMASALA(PLAINJARDA)14250KG. 22 MANUFACTURING SMALL 15 Local - 100%, Foreign - 0% 5 2078 MADHESH 0.310 3 0.0 0.0 0.0 0.0 0.0 1.0 0.0
498 SHIVASHAKTIOILANDFATS(OIL) BARA 500000000 300000000 200000000 REFINEDSOYABEANOIL30000MT.REFINEDSUNFLOWEROIL3... 60 MANUFACTURING MEDIUM 1000 Local - 100%, Foreign - 0% 5 2078 MADHESH 0.310 2 0.0 0.0 0.0 0.0 0.0 1.0 0.0
499 SHERPAOUTDOORSPORTSGOODSINDUSTRIES KATHMANDU 170000000 150000000 20000000 READYMADEGARMENT&TREKKINGGOODSSUCHASSLEEPINGBA... 200 MANUFACTURING SMALL 300 Local - 100%, Foreign - 0% 5 2078 BAGMATI 0.310 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0

500 rows × 23 columns

In [57]:
#@title check for any null value after mapping
display(df3[df3['YEAR'].isnull()])
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
In [58]:
#@title info after encode
df3.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 500 entries, 0 to 499
Data columns (total 23 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   INDUSTRY NAME                500 non-null    object 
 1   DISTRICT                     500 non-null    object 
 2   TOTAL CAPITAL                500 non-null    int64  
 3   FIXED CAPITAL                500 non-null    int64  
 4   WORKING CAPITAL              500 non-null    int64  
 5   PRODUCT AND ANNUAL CAPACITY  500 non-null    object 
 6   EMPLOYMENT                   500 non-null    int64  
 7   CATEGORY                     500 non-null    object 
 8   SCALE                        500 non-null    object 
 9   POWER                        500 non-null    int64  
 10  % OF INVESTMENT              500 non-null    object 
 11  MONTH                        500 non-null    int64  
 12  YEAR                         500 non-null    int64  
 13  PROVINCE                     500 non-null    object 
 14  CATEGORY_FREQ                500 non-null    float64
 15  SCALE_encode                 500 non-null    int64  
 16  PROVINCE_BAGMATI             500 non-null    float64
 17  PROVINCE_GANDAKI             500 non-null    float64
 18  PROVINCE_KARNALI             500 non-null    float64
 19  PROVINCE_KOSHI               500 non-null    float64
 20  PROVINCE_LUMBINI             500 non-null    float64
 21  PROVINCE_MADHESH             500 non-null    float64
 22  PROVINCE_SUDUR-PASCHIM       500 non-null    float64
dtypes: float64(8), int64(8), object(7)
memory usage: 90.0+ KB
In [59]:
#@title save for visualization
df3.to_csv('3_encode.csv', index=False)
In [60]:
#@title copy for model
df4=df3.copy(deep=True)

model¶

In [61]:
#@title import scipy
from scipy.stats import iqr
In [62]:
#@title apply interquartile range with scipy in total capital. fixed capital and working capital

# calculate IQR for specified columns
for col in ['TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL']:
    q1 = df4[col].quantile(0.25)
    q3 = df4[col].quantile(0.75)
    iqr_val = iqr(df4[col])
    print(f"IQR for {col}: {iqr_val}\n")

    # filtering out outliers
    upper_bound = q3 + 1.5 * iqr_val
    lower_bound = q1 - 1.5 * iqr_val
    df4 = df4[(df4[col] >= lower_bound) & (df4[col] <= upper_bound)]
IQR for TOTAL CAPITAL: 257600000.0

IQR for FIXED CAPITAL: 135000000.0

IQR for WORKING CAPITAL: 48200000.0

In [63]:
#@ title import plotly
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [64]:
#@title create box plots before and after IQR treatment
fig_before = px.box(df2, y=['TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL'],
                    title='Box Plots Before IQR Treatment')
fig_before.show()

fig_after = px.box(df4, y=['TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL'],
                   title='Box Plots After IQR Treatment')
fig_after.show()
In [65]:
#@title apply standard scaler
from sklearn.preprocessing import StandardScaler

# intitailize
scaler = StandardScaler()

#  columns to standardize
columns_to_standardize = ['TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL']

# standardized columns into new columns.
for col in columns_to_standardize:
  # fit scaler
  scaler.fit(df4[[col]])

  # new column with the standardized values
  df4[col + '_standardized'] = scaler.transform(df4[[col]])
In [66]:
#@title split investment
def split_investment(investment_str):
    local_pct = 0
    foreign_pct = 0
    try:
      parts = investment_str.split(',')
      for part in parts:
          if 'Local' in part:
              local_pct = float(part.split('-')[1].replace('%', '').strip())
          elif 'Foreign' in part:
              foreign_pct = float(part.split('-')[1].replace('%', '').strip())
    except:
        print(f"Error processing: {investment_str}")
        return local_pct, foreign_pct
    return local_pct, foreign_pct

df4[['Local_Investment', 'Foreign_Investment']] = df2['% OF INVESTMENT'].apply(lambda x: pd.Series(split_investment(x)))
In [67]:
#@title display after split
display(df4[['% OF INVESTMENT', 'Local_Investment', 'Foreign_Investment']].sample(5))
% OF INVESTMENT Local_Investment Foreign_Investment
459 Local - 100%, Foreign - 0% 100.0 0.0
264 Local - 100%, Foreign - 0% 100.0 0.0
158 Local - 0%, Foreign - 100% 0.0 100.0
51 Local - 0%, Foreign - 100% 0.0 100.0
73 Local - 0%, Foreign - 100% 0.0 100.0
In [68]:
#@title unique values for Local and Foreign Investment
print("Unique Local Investment values:")
display(df4['Local_Investment'].value_counts())
print("\nUnique Foreign Investment values:")
display(df4['Foreign_Investment'].value_counts())
Unique Local Investment values:
Local_Investment
0.000      190
100.000    153
40.000       4
51.000       3
20.000       3
6.250        2
15.000       2
36.000       1
34.221       1
50.000       1
5.670        1
9.910        1
10.000       1
15.240       1
13.040       1
49.000       1
16.667       1
20.290       1
66.420       1
6.000        1
Name: count, dtype: int64
Unique Foreign Investment values:
Foreign_Investment
100.000    189
0.000      155
60.000       4
80.000       3
49.000       3
85.000       2
93.750       2
83.333       1
51.000       1
79.710       1
33.580       1
84.760       1
86.960       1
64.000       1
90.000       1
94.330       1
50.000       1
65.779       1
94.000       1
Name: count, dtype: int64
In [69]:
#@title view list of columns
columns = df4.columns.tolist()
print("Columns:", columns)
Columns: ['INDUSTRY NAME', 'DISTRICT', 'TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL', 'PRODUCT AND ANNUAL CAPACITY', 'EMPLOYMENT', 'CATEGORY', 'SCALE', 'POWER', '% OF INVESTMENT', 'MONTH', 'YEAR', 'PROVINCE', 'CATEGORY_FREQ', 'SCALE_encode', 'PROVINCE_BAGMATI', 'PROVINCE_GANDAKI', 'PROVINCE_KARNALI', 'PROVINCE_KOSHI', 'PROVINCE_LUMBINI', 'PROVINCE_MADHESH', 'PROVINCE_SUDUR-PASCHIM', 'TOTAL CAPITAL_standardized', 'FIXED CAPITAL_standardized', 'WORKING CAPITAL_standardized', 'Local_Investment', 'Foreign_Investment']
In [70]:
#@title correlation matrix using only numerical columns
correlation_matrix = df4.select_dtypes(include=['number']).corr()

# a heatmap of the correlation matrix
fig = px.imshow(correlation_matrix,
                labels=dict(x="Features", y="Features", color="Correlation"),
                x=correlation_matrix.columns,
                y=correlation_matrix.columns,
                color_continuous_scale='RdBu',
                zmin=-1, zmax=1,
                title='Correlation Matrix of Numerical Features')
fig.show()
In [71]:
# @title features and target
features = ['TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL',
            'POWER', 'SCALE_encode',
            'PROVINCE_BAGMATI', 'PROVINCE_GANDAKI', 'PROVINCE_KOSHI',
            'PROVINCE_LUMBINI','PROVINCE_MADHESH', 'PROVINCE_SUDUR-PASCHIM',
            'CATEGORY_FREQ','Local_Investment', 'Foreign_Investment']
target = 'CATEGORY'
In [72]:
X = df4[features]
y = df4[target]
In [73]:
#@title import and train test splot
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
In [74]:
#@title metrics function accuracy and classification
from sklearn.metrics import accuracy_score, classification_report

# Function to train and evaluate a model
def evaluate_model(model, X_train, X_test, y_train, y_test):
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    report = classification_report(y_test, y_pred)
    print(f"Model: {model.__class__.__name__}")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"Classification Report:\n{report}\n")
    return accuracy, report
In [75]:
# @title Import models
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
In [76]:
#@title list of models to evaluate
models = [
    LogisticRegression(max_iter=1000, random_state=42),
    RandomForestClassifier(random_state=42),
]
In [77]:
# @title evaluate each model
results = {}  # Store results for comparison
for model in models:
    accuracy, report = evaluate_model(model, X_train, X_test, y_train, y_test)
    results[model.__class__.__name__] = {'accuracy': accuracy, 'report': report}
Model: LogisticRegression
Accuracy: 0.6216
Classification Report:
                        precision    recall  f1-score   support

     AGRO AND FORESTRY       0.00      0.00      0.00         7
                ENERGY       0.00      0.00      0.00         1
INFORMATION TECHNOLOGY       0.00      0.00      0.00         2
        INFRASTRUCTURE       0.00      0.00      0.00         1
         MANUFACTURING       0.62      0.89      0.73        18
               SERVICE       0.70      0.41      0.52        17
               TOURISM       0.61      0.82      0.70        28

              accuracy                           0.62        74
             macro avg       0.27      0.30      0.28        74
          weighted avg       0.54      0.62      0.56        74


Model: RandomForestClassifier
Accuracy: 0.9595
Classification Report:
                        precision    recall  f1-score   support

     AGRO AND FORESTRY       0.83      0.71      0.77         7
                ENERGY       0.50      1.00      0.67         1
INFORMATION TECHNOLOGY       1.00      1.00      1.00         2
        INFRASTRUCTURE       0.00      0.00      0.00         1
         MANUFACTURING       1.00      1.00      1.00        18
               SERVICE       1.00      1.00      1.00        17
               TOURISM       0.97      1.00      0.98        28

              accuracy                           0.96        74
             macro avg       0.76      0.82      0.77        74
          weighted avg       0.95      0.96      0.95        74


In [78]:
#@title save for visualization
df3.to_csv('4_visuals.csv', index=False)

visualization¶

In [79]:
#@title for visuals
df0=pd.read_csv('4_visuals.csv')
In [80]:
#@title 'Scale' vs 'Employment', by 'District'
fig = px.bar(df0, x='EMPLOYMENT', y='SCALE', color='PROVINCE',
                 title="Employment vs Scale by Province",
                 labels={'Province': 'Province Name'})
fig.show()
In [81]:
#@title 'Scale' vs 'Power', by 'District'
fig = px.bar(df0, x='POWER', y='SCALE', color='PROVINCE',
                 title="Power vs Scale by Province",
                 labels={'Province': 'Province Name'})
fig.show()
In [82]:
#@title Scatter plot of TOTAL CAPITAL vs. EMPLOYMENT
fig = px.scatter(df0, x="TOTAL CAPITAL", y="FIXED CAPITAL",
                 title="Total Capital vs. Employment",
                 hover_data=['CATEGORY'])
fig.show()
In [83]:
print(df4.columns)
print(df4.shape)
Index(['INDUSTRY NAME', 'DISTRICT', 'TOTAL CAPITAL', 'FIXED CAPITAL',
       'WORKING CAPITAL', 'PRODUCT AND ANNUAL CAPACITY', 'EMPLOYMENT',
       'CATEGORY', 'SCALE', 'POWER', '% OF INVESTMENT', 'MONTH', 'YEAR',
       'PROVINCE', 'CATEGORY_FREQ', 'SCALE_encode', 'PROVINCE_BAGMATI',
       'PROVINCE_GANDAKI', 'PROVINCE_KARNALI', 'PROVINCE_KOSHI',
       'PROVINCE_LUMBINI', 'PROVINCE_MADHESH', 'PROVINCE_SUDUR-PASCHIM',
       'TOTAL CAPITAL_standardized', 'FIXED CAPITAL_standardized',
       'WORKING CAPITAL_standardized', 'Local_Investment',
       'Foreign_Investment'],
      dtype='object')
(370, 28)
In [84]:
print(df0.columns)
print(df0.shape)
Index(['INDUSTRY NAME', 'DISTRICT', 'TOTAL CAPITAL', 'FIXED CAPITAL',
       'WORKING CAPITAL', 'PRODUCT AND ANNUAL CAPACITY', 'EMPLOYMENT',
       'CATEGORY', 'SCALE', 'POWER', '% OF INVESTMENT', 'MONTH', 'YEAR',
       'PROVINCE', 'CATEGORY_FREQ', 'SCALE_encode', 'PROVINCE_BAGMATI',
       'PROVINCE_GANDAKI', 'PROVINCE_KARNALI', 'PROVINCE_KOSHI',
       'PROVINCE_LUMBINI', 'PROVINCE_MADHESH', 'PROVINCE_SUDUR-PASCHIM'],
      dtype='object')
(500, 23)
In [85]:
# @title Bar chart of CATEGORY counts
fig = px.histogram(df0, x="CATEGORY",
                   title="Distribution of Categories")
fig.show()
In [86]:
# @title Box plot of TOTAL CAPITAL by CATEGORY
fig = px.box(df0, x="CATEGORY", y="TOTAL CAPITAL",
              title="Total Capital by Category")
fig.show()
In [87]:
# @title EMPLOYMENT

fig = go.Figure()
fig.add_trace(go.Scatter(x=df0.index, y=df0['EMPLOYMENT'],
                         mode='lines', name='EMPLOYMENT'))

fig.update_layout(title='EMPLOYMENT',
                  xaxis_title='Index',
                  yaxis_title='EMPLOYMENT')

fig.update_xaxes(showline=True, linewidth=1, linecolor='black', mirror=False)
fig.update_yaxes(showline=True, linewidth=1, linecolor='black', mirror=False)


fig.show()
In [88]:
# @title CATEGORY
fig = px.bar(df0.groupby('CATEGORY').size().reset_index(name='count'),
             y='CATEGORY', x='count', orientation='h',
             color='CATEGORY',
             title='Distribution of Categories',
             text='count')  # Add the 'text' parameter

fig.update_layout(showlegend=False) # Hide legend if not needed
fig.update_xaxes(showline=True, linewidth=1, linecolor='black', mirror=False)
fig.update_yaxes(showline=True, linewidth=1, linecolor='black', mirror=False)

fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')  # Format and position the text
fig.show()
In [89]:
# @title SCALE
fig = px.bar(df0.groupby('SCALE').size().reset_index(name='count'),
             x='SCALE', y='count',
             color='SCALE',
             title='Distribution of Scales',
             text='count')

fig.update_layout(
    xaxis_title='Scale',
    yaxis_title='Count',
    plot_bgcolor='rgba(0,0,0,0)',
    xaxis={'showline': True, 'linewidth': 1, 'linecolor': 'black'},
    yaxis={'showline': True, 'linewidth': 1, 'linecolor': 'black'},
)
fig.update_traces(marker_line_width=1, marker_line_color='black',
                  texttemplate='%{text:.2s}', textposition='outside')
fig.show()
In [90]:
# @title Stacked Bar Chart with Range Slider (Interval)
fig = px.bar(df4, x=df4.index, y=['TOTAL CAPITAL','FIXED CAPITAL', 'WORKING CAPITAL'],
             title='Total, Fixed, and Working Capital',
             labels={'value': 'Capital', 'variable': 'Capital Type'})

fig.update_layout(
    xaxis=dict(
        rangeslider=dict(visible=True),
        type="linear",
        range=[00,100]
    )
)
fig.show()
In [91]:
#@title Employment by Scale, Category, Province and District in sun burst chart
fig = px.sunburst(df0, path=['SCALE', 'CATEGORY','PROVINCE','DISTRICT'], values='EMPLOYMENT',
                  title='Employment by Scale, Category, Province and District', height=800, width=1000)
fig.show()
In [92]:
# @title employment by district
df_employment = df0.groupby('CATEGORY')['EMPLOYMENT'].sum().sort_values(ascending=False)

fig = px.pie(df_employment, values='EMPLOYMENT', names=df_employment.index,
             title='Employment by Category')
fig.show()
In [93]:
# @title stacked area chart
fig = px.bar(df0, x="CATEGORY", y="POWER",
             title="Power Consumption by Category")
fig.show()
In [94]:
#@title power consumption by category
fig = px.pie(df0, values='POWER', names='CATEGORY', hole=0.4,
             title='Power Consumption by Category')
fig.update_traces(textposition='outside', textinfo='percent+label')
fig.show()
In [95]:
#@title nightingale the values for each category
category_values = df4.groupby('CATEGORY')['TOTAL CAPITAL_standardized'].sum()

# Create the Nightingale rose chart
fig = go.Figure(go.Barpolar(
    r=category_values,
    theta=category_values.index,
    width=[0.8] * len(category_values),
    marker_color=px.colors.qualitative.Plotly,
))

fig.update_layout(
    title="Nightingale Rose Chart of Total Capital by Category",
    polar=dict(
        radialaxis=dict(
            visible=True,
        ),
    ),
    showlegend=True
)
fig.show()
In [96]:
# @title Treemap Visualization
fig = px.treemap(df0, path=[ 'SCALE','PROVINCE', 'CATEGORY'], values='EMPLOYMENT',
                  color='TOTAL CAPITAL', hover_data=['POWER'],
                  color_continuous_scale='RdBu',
                  title="Treemap of Employment by Category, District, and Scale")
fig.show()
In [97]:
#@title treemap total capital by category and scale
fig = px.treemap(df0, path=['SCALE','CATEGORY'], values='TOTAL CAPITAL',
                  title='Treemap of Total Capital by Category and Scale')
fig.show()
In [98]:
import plotly.graph_objects as go

# Initialize lists to store link data
source = []
target = []
value = []

# Iterate through dataframe and create links based on values
for index, row in df0.iterrows():
    source_index = list(df0['CATEGORY'].unique()).index(row['CATEGORY'])
    target_index = len(df0['CATEGORY'].unique()) + list(df0['SCALE'].unique()).index(row['SCALE'])
    source.append(source_index)
    target.append(target_index)
    value.append(row['TOTAL CAPITAL'])

    target_index_2 = len(df0['CATEGORY'].unique()) + len(df0['SCALE'].unique()) + list(df0['PROVINCE'].unique()).index(row['PROVINCE'])
    source.append(target_index)
    target.append(target_index_2)
    value.append(row['EMPLOYMENT'])

# Create the Sankey diagram with the pre-populated link data
fig = go.Figure(data=[go.Sankey(
    node=dict(
        pad=15,
        thickness=20,
        line=dict(color="black", width=0.5),
        label=df0['CATEGORY'].unique().tolist() + df0['SCALE'].unique().tolist() + df0['PROVINCE'].unique().tolist(),  # Combine all categories
        color="blue"
    ),
    link=dict(
        source=source,  # Assign the pre-populated source list
        target=target,  # Assign the pre-populated target list
        value=value  # Assign the pre-populated value list
    ))])

fig.update_layout(title_text="Network Spider Web Diagram", font_size=10)
fig.show()
In [99]:
#@title network
import plotly.graph_objects as go
import networkx as nx

# Create a graph from the DataFrame (df2)
graph = nx.from_pandas_edgelist(df0, source='DISTRICT', target='CATEGORY', edge_attr=True)

# Create a Plotly graph object
fig = go.Figure(data=[go.Scatter(
    x=[pos[0] for pos in nx.spring_layout(graph).values()],
    y=[pos[1] for pos in nx.spring_layout(graph).values()],
    mode='markers+text',
    text=list(graph.nodes),
    marker=dict(
        size=10,
        color='blue'
    )
)])

# Customize the layout
fig.update_layout(
    title="District-Category Network Graph",
    width=1000,  # Adjust width
    height=800,  # Adjust height
    showlegend=False,
)

fig.show()
In [100]:
# @title Funnel Chart
employment_by_category = df0.groupby('PROVINCE')['EMPLOYMENT'].sum().reset_index()

fig = go.Figure(go.Funnel(
    y = employment_by_category['PROVINCE'],
    x = employment_by_category['EMPLOYMENT'],
    textposition = "inside",
    textinfo = "value+percent initial",
))
fig.update_layout(title="Funnel Chart of Employment by Proince")
fig.show()
In [101]:
# @title Violin Plots for Employment by District
fig = px.violin(df0, x='PROVINCE', y='EMPLOYMENT', color='CATEGORY', box=True, points='all',
                title='Distribution of Employment by District and Category')
fig.show()
In [102]:
# @title Box Plots for Capital by Category
fig = px.box(df0, x='PROVINCE', y='CATEGORY', color='SCALE',
             title='Distribution of Total Capital by Category and Scale')
fig.show()
In [103]:
# @title Parallel Categories Plot with more dimensions
fig = px.parallel_categories(df0, dimensions=['CATEGORY', 'PROVINCE', 'SCALE'], color="EMPLOYMENT",
                             title='Parallel Categories Plot of Key Metrics')
fig.show()
In [104]:
# @title sepearte dataframes based on different province , categories and scale

provinces = df0['PROVINCE'].unique()
categories = df0['CATEGORY'].unique()
scales = df0['SCALE'].unique()

separated_dfs = {}

for province in provinces:
  for category in categories:
    for scale in scales:
      key = f"{province}_{category}_{scale}"
      separated_dfs[key] = df0[
          (df0['PROVINCE'] == province) &
          (df0['CATEGORY'] == category) &
          (df0['SCALE'] == scale)
      ].copy()


# Example usage to show the first 5 rows of each DataFrame:
for key, df in separated_dfs.items():
    if not df.empty:
        print(f"\nDataFrame for {key}:")
        display(df.head(2))
    else:
        print(f"\nDataFrame for {key}: (Empty)")
DataFrame for BAGMATI_SERVICE_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
0 HUALICONSTRUCTIONANDENGINEERING BHAKTAPUR 150000000 87000000 63000000 VARIOUSKINDSOFCONSTRUCTIONWORKS(CONSTRUCTIONRE... 88 SERVICE SMALL 10 Local - 0%, Foreign - 100% 4 2076 BAGMATI 0.166 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
2 MOKSHAINTERNATIONALCARGO KATHMANDU 50000000 46000000 4000000 INTERNATIONALCARGOHANDLING12000MT 50 SERVICE SMALL 10 Local - 0%, Foreign - 100% 5 2076 BAGMATI 0.166 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_SERVICE_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
90 CRCCFOURTEENNEPAL SINDHUPALCHOWK 1000000000 500000000 500000000 CONSTRUCTIONWORKS(ONVARIOUSSECTORSLIKEROADBRID... 330 SERVICE LARGE 25 Local - 0%, Foreign - 100% 7 2076 BAGMATI 0.166 1 1.0 0.0 0.0 0.0 0.0 0.0 0.0
222 PEOPLESDENTALCOLLEGE&HOSPITAL KATHMANDU 616000000 586000000 30000000 FOURBEDSWARDS80BED 181 SERVICE LARGE 400 Local - 100%, Foreign - 0% 12 2076 BAGMATI 0.166 1 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_SERVICE_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
3 TENGFEICONSTRUCTIONCOMPANY KATHMANDU 300000000 227000000 73000000 CONSTRUCTIONWORKSOFVARIOUSTYPE1000000000L.S 375 SERVICE MEDIUM 100 Local - 0%, Foreign - 100% 5 2076 BAGMATI 0.166 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
6 A.B.C.INTERNATIONALCARGO KATHMANDU 150000000 141000000 9000000 INTERNATIONALCARGOLANDLING30000MT 64 SERVICE MEDIUM 15 Local - 0%, Foreign - 100% 5 2076 BAGMATI 0.166 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_ENERGY_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
264 DIPJOYOTIHYDROPOWER DOLKHA 117500000 102500000 15000000 HYDROELECTRICITY550K.W. 10 ENERGY SMALL 35 Local - 100%, Foreign - 0% 4 2077 BAGMATI 0.144 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_ENERGY_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
63 MILAREPAENERGY SINDHUPALCHOWK 4040000000 3997000000 43000000 23.6M.W. 30 ENERGY LARGE 100 Local - 100%, Foreign - 0% 7 2076 BAGMATI 0.144 1 1.0 0.0 0.0 0.0 0.0 0.0 0.0
88 UPPERBALEPHIHYDROPOWERLIMITED SINDHUPALCHOWK 5800000000 5732319033 67680967 46M.W. 70 ENERGY LARGE 50 Local - 100%, Foreign - 0% 7 2076 BAGMATI 0.144 1 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_ENERGY_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
239 MELAMCHIHYDRO SINDHUPALCHOWK 197299619 188121380 9178239 HYDROPOWER998KW. 10 ENERGY MEDIUM 50 Local - 100%, Foreign - 0% 3 2077 BAGMATI 0.144 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
291 PATHIBHARAHYDROPOWER DOLKHA 284620030 269057100 15562930 HYDROELECTRICITY1.1M.W. 28 ENERGY MEDIUM 25 Local - 100%, Foreign - 0% 6 2077 BAGMATI 0.144 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_INFORMATION TECHNOLOGY_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
21 ZEGALNEPAL LALITPUR 5000000 3000000 2000000 SOFTWAREDEVELOPMENT100PACKAGES 13 INFORMATION TECHNOLOGY SMALL 12 Local - 0%, Foreign - 100% 5 2076 BAGMATI 0.05 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
87 BEY0NDIDNEPAL KATHMANDU 5000000 3000000 2000000 SOFTWAREDEVELOPMENT100PACKAGES 13 INFORMATION TECHNOLOGY SMALL 12 Local - 0%, Foreign - 100% 7 2076 BAGMATI 0.05 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_INFORMATION TECHNOLOGY_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
102 VIANETCOMMUNICATIONSLTD LALITPUR 840000000 785600000 54400000 HOME53742PERSONSMALLOFFICE/HOMEOFFICE3634\nPER... 510 INFORMATION TECHNOLOGY LARGE 100 Local - 100%, Foreign - 0% 8 2076 BAGMATI 0.05 1 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_INFORMATION TECHNOLOGY_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
4 S.W.SOFTWARE LALITPUR 250000000 227000000 23000000 SOFTWAREDEVELOPMENT350PACKAGE 83 INFORMATION TECHNOLOGY MEDIUM 25 Local - 0%, Foreign - 100% 5 2076 BAGMATI 0.05 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
79 D.D.L.SOFTWARECOMPANY LALITPUR 250000000 240000000 10000000 SOFTWAREDEVELOPMENT—450PACKAGES 120 INFORMATION TECHNOLOGY MEDIUM 30 Local - 0%, Foreign - 100% 7 2076 BAGMATI 0.05 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_TOURISM_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
15 HONGYUNVEGETERIANRESTAURANT LALITPUR 100000000 92000000 8000000 HOTEL40BEDSRESTAURANT80SEATS 35 TOURISM SMALL 30 Local - 0%, Foreign - 100% 5 2076 BAGMATI 0.258 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
20 MALAHOTELANDRESTAURANT KATHMANDU 50000000 47000000 3000000 HOTEL28BEDSRESTAURANT50SEATS 30 TOURISM SMALL 50 Local - 0%, Foreign - 100% 5 2076 BAGMATI 0.258 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_TOURISM_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
16 BALAJIHOLDINGSANDHOTEL KATHMANDU 848000000 833000000 15000000 HOTEL98BEDSRESTAURANT200SEATS 46 TOURISM LARGE 500 Local - 100%, Foreign - 0% 5 2076 BAGMATI 0.258 1 1.0 0.0 0.0 0.0 0.0 0.0 0.0
56 MARGARETHOTELANDRESTAURANT KATHMANDU 300000000 290000000 10000000 HOTEL48BEDSRESTAURANT60SEATS 45 TOURISM LARGE 150 Local - 0%, Foreign - 100% 6 2076 BAGMATI 0.258 1 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_TOURISM_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
5 QINGYUNHOTEL KATHMANDU 250000000 245000000 5000000 HOTEL48BEDS 45 TOURISM MEDIUM 50 Local - 0%, Foreign - 100% 5 2076 BAGMATI 0.258 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
11 HOTELLIFANG KATHMANDU 150000000 145000000 5000000 HOTEL40BEDSRESTAURANT50SEATS 40 TOURISM MEDIUM 75 Local - 0%, Foreign - 100% 5 2076 BAGMATI 0.258 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_MANUFACTURING_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
17 NEPALSANYOUDOORSANDWINDOWSCOMPANY BHAKTAPUR 160000000 99210000 60790000 ALUMINIUMDOORSANDWINDOWS189000SQ.FT. 58 MANUFACTURING SMALL 100 Local - 6.25%, Foreign - 93.75% 5 2076 BAGMATI 0.31 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
36 SARALURJANEPAL KATHMANDU 40000000 23400000 16600000 SOLARPVSYSTEMFORHOMEAPPLIANCE1000SEATSSOLARPVS... 58 MANUFACTURING SMALL 40 Local - 66.42%, Foreign - 33.58% 6 2076 BAGMATI 0.31 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_MANUFACTURING_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
61 DERRENPHARMACEUTICALS LALITPUR 1301973000 1155534000 146439000 TABLETS250MILLIONCAPSULES8MILLION\n0NIMENTS35M... 184 MANUFACTURING LARGE 500 Local - 100%, Foreign - 0% 7 2076 BAGMATI 0.31 1 1.0 0.0 0.0 0.0 0.0 0.0 0.0
484 SARASBEVERAGE DHADING 2000000000 1200000000 800000000 JUICE10000KLCARBONATEDSOFTDRINKS20000KLENERGYD... 180 MANUFACTURING LARGE 1000 Local - 100%, Foreign - 0% 4 2078 BAGMATI 0.31 1 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_MANUFACTURING_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
10 DREAMPAINTSNEPAL MAKWANPUR 255720000 135720000 120000000 PAINT8400KLPUTTY670M.T. 92 MANUFACTURING MEDIUM 500 Local - 100%, Foreign - 0% 5 2076 BAGMATI 0.31 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
19 LIFEFOODANDBEVERAGE CHITWAN 249500000 171500000 78000000 FRUITANDVEGETABLEJUICE25000KLENERGYDRINK(NONAL... 142 MANUFACTURING MEDIUM 625 Local - 100%, Foreign - 0% 5 2076 BAGMATI 0.31 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_AGRO AND FORESTRY_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
42 JYAMRUNGAGRICULTUREFARM DHADING 5000000 4050000 950000 VEGETABLES78MTFRUITS14MT 20 AGRO AND FORESTRY SMALL 15 Local - 0%, Foreign - 100% 6 2076 BAGMATI 0.066 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
134 LOTUSCOLDSTORAGE KATHMANDU 50000000 42000000 8000000 COLDSTORAGEOFVAGETABLESANDFRUITS-6000MT 35 AGRO AND FORESTRY SMALL 200 Local - 0%, Foreign - 100% 8 2076 BAGMATI 0.066 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_AGRO AND FORESTRY_LARGE: (Empty)

DataFrame for BAGMATI_AGRO AND FORESTRY_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
100 MANIKHELJADIBUTIFARM LALITPUR 100000000 89265000 10735000 HERBALPOWDER300MTESSENTIALOIL30000LITERDRIEDHE... 58 AGRO AND FORESTRY MEDIUM 200 Local - 0%, Foreign - 100% 8 2076 BAGMATI 0.066 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
101 CHINESEHERBALUDHOGLTD LALITPUR 150000000 95900000 54100000 HERBALPOWDER550MTESSENTIALOIL35000LITERDRIEDHE... 114 AGRO AND FORESTRY MEDIUM 400 Local - 0%, Foreign - 100% 8 2076 BAGMATI 0.066 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_MINERAL_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
347 BABAMINES&MINERALS MAKWANPUR 123446032 112101500 11344532 LIMESTONE480000M.T.MARBLEBLOCK150MCUBEMARBLECH... 92 MINERAL SMALL 800 Local - 100%, Foreign - 0% 9 2077 BAGMATI 0.002 3 1.0 0.0 0.0 0.0 0.0 0.0 0.0
DataFrame for BAGMATI_MINERAL_LARGE: (Empty)

DataFrame for BAGMATI_MINERAL_MEDIUM: (Empty)

DataFrame for BAGMATI_INFRASTRUCTURE_SMALL: (Empty)

DataFrame for BAGMATI_INFRASTRUCTURE_LARGE: (Empty)

DataFrame for BAGMATI_INFRASTRUCTURE_MEDIUM: (Empty)

DataFrame for KOSHI_SERVICE_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
382 ARCHIELABELPRINTERS MORANG 155600000 120600000 35000000 LABELIMPRESSION(1.15MTOR75000SEATPERDAY)318M.T. 33 SERVICE SMALL 200 Local - 100%, Foreign - 0% 11 2077 KOSHI 0.166 3 0.0 0.0 0.0 1.0 0.0 0.0 0.0
DataFrame for KOSHI_SERVICE_LARGE: (Empty)

DataFrame for KOSHI_SERVICE_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
234 MAHARISHIVEDICINSTITUTE(1) JHAPA 510000000 490000000 20000000 SANSKRITLANGUAGETRAINING3000NOS.NEPALILANGUAGE... 75 SERVICE MEDIUM 100 Local - 0%, Foreign - 100% 3 2077 KOSHI 0.166 2 0.0 0.0 0.0 1.0 0.0 0.0 0.0
DataFrame for KOSHI_ENERGY_SMALL: (Empty)

DataFrame for KOSHI_ENERGY_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
1 CHISANGHYDRO MORANG 304505000 296587693 7917307 Hydroelectricproduction1.8MW 31 ENERGY LARGE 30 Local - 100%, Foreign - 0% 4 2076 KOSHI 0.144 1 0.0 0.0 0.0 1.0 0.0 0.0 0.0
13 HYDROCONNECTION SOLUKHUMBU 3124544230 3022138750 102405480 Hydropower18MW. 68 ENERGY LARGE 100 Local - 100%, Foreign - 0% 5 2076 KOSHI 0.144 1 0.0 0.0 0.0 1.0 0.0 0.0 0.0
DataFrame for KOSHI_ENERGY_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
194 HALESIURJA KHOTANG 420420000 410559320 9860680 2.2M.W. 17 ENERGY MEDIUM 50 Local - 100%, Foreign - 0% 11 2076 KOSHI 0.144 2 0.0 0.0 0.0 1.0 0.0 0.0 0.0
DataFrame for KOSHI_INFORMATION TECHNOLOGY_SMALL: (Empty)

DataFrame for KOSHI_INFORMATION TECHNOLOGY_LARGE: (Empty)

DataFrame for KOSHI_INFORMATION TECHNOLOGY_MEDIUM: (Empty)

DataFrame for KOSHI_TOURISM_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
156 CELESTIALRESORTS ILAM 100000000 95000000 5000000 HOTEL30BEDSRESTAURANT50SEATS 35 TOURISM SMALL 70 Local - 0%, Foreign - 100% 9 2076 KOSHI 0.258 3 0.0 0.0 0.0 1.0 0.0 0.0 0.0
238 FATTSERNVEGRESTAURANT SUNSARI 50000000 33000000 17000000 RESTAURANT40SEATS 18 TOURISM SMALL 50 Local - 0%, Foreign - 100% 3 2077 KOSHI 0.258 3 0.0 0.0 0.0 1.0 0.0 0.0 0.0
DataFrame for KOSHI_TOURISM_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
453 MOUNTEVERESTCABLECAR SOLUKHUMBU 660000000 625000000 35000000 CABLECAR1120000PERSONS 68 TOURISM LARGE 2000 Local - 100%, Foreign - 0% 3 2078 KOSHI 0.258 1 0.0 0.0 0.0 1.0 0.0 0.0 0.0
DataFrame for KOSHI_TOURISM_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
126 VEGASRECREATIONNEPAL JHAPA 250000000 192000000 58000000 CASINOPLAYERS200000PERSONS 355 TOURISM MEDIUM 150 Local - 100%, Foreign - 0% 8 2076 KOSHI 0.258 2 0.0 0.0 0.0 1.0 0.0 0.0 0.0
191 NEPALIRIKAHOTEL MORANG 222925000 207900000 15025000 HOTEL48BEDSRESTAURANT100SEATS 39 TOURISM MEDIUM 100 Local - 100%, Foreign - 0% 11 2076 KOSHI 0.258 2 0.0 0.0 0.0 1.0 0.0 0.0 0.0
DataFrame for KOSHI_MANUFACTURING_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
200 KANKAIRUBBERINDUSTRIES JHAPA 200000000 130000000 70000000 RUBBERPRODUCTS(SH0ESSOLEDOORMATBELTSTRAPWASHER... 80 MANUFACTURING SMALL 300 Local - 100%, Foreign - 0% 11 2076 KOSHI 0.31 3 0.0 0.0 0.0 1.0 0.0 0.0 0.0
204 JAYSHREEPOLYMERS(1) SUNSARI 136405000 106405000 30000000 SLIPPER1800000PAIR 32 MANUFACTURING SMALL 500 Local - 100%, Foreign - 0% 11 2076 KOSHI 0.31 3 0.0 0.0 0.0 1.0 0.0 0.0 0.0
DataFrame for KOSHI_MANUFACTURING_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
393 PRESIDENTBI0TECHNEPAL SUNSARI 1750000000 1253600000 496400000 0RGANICFERTILIZER3000.Z. 30 MANUFACTURING LARGE 1200 Local - 15%, Foreign - 85% 12 2077 KOSHI 0.31 1 0.0 0.0 0.0 1.0 0.0 0.0 0.0
DataFrame for KOSHI_MANUFACTURING_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
14 PHARMONICSLIFESCIENCES SUNSARI 350000000 240500000 109500000 TABLETS100MILLION\nCAPSULES50MILLIONOINTMENTS2... 45 MANUFACTURING MEDIUM 500 Local - 100%, Foreign - 0% 5 2076 KOSHI 0.31 2 0.0 0.0 0.0 1.0 0.0 0.0 0.0
135 ASIACHEM MORANG 245000000 190000000 55000000 PRINTEDPACKAGINGWRAPPER2000M.T. 80 MANUFACTURING MEDIUM 500 Local - 100%, Foreign - 0% 8 2076 KOSHI 0.31 2 0.0 0.0 0.0 1.0 0.0 0.0 0.0
DataFrame for KOSHI_AGRO AND FORESTRY_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
214 NAYABAZAARKRISHIFARM ILAM 135000000 126500000 8500000 CTCTEA75M.T.ORTHODOXTEA275M.T.COFFEE20M.T. 54 AGRO AND FORESTRY SMALL 380 Local - 100%, Foreign - 0% 11 2076 KOSHI 0.066 3 0.0 0.0 0.0 1.0 0.0 0.0 0.0
317 MARUTIDAIRYPRODUCT MORANG 150000000 116000000 34000000 PASTEURIZEDSKIMMEDMILK2200000LITERCURD100000LI... 27 AGRO AND FORESTRY SMALL 350 Local - 100%, Foreign - 0% 7 2077 KOSHI 0.066 3 0.0 0.0 0.0 1.0 0.0 0.0 0.0
DataFrame for KOSHI_AGRO AND FORESTRY_LARGE: (Empty)

DataFrame for KOSHI_AGRO AND FORESTRY_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
312 ARIHANTAGRIFARM&RESEARCHCENTER SUNSARI 220000000 170000000 50000000 GREENVEGETABLES50M.T.FRUITS100M.T.GRAINS(RICEW... 264 AGRO AND FORESTRY MEDIUM 200 Local - 100%, Foreign - 0% 7 2077 KOSHI 0.066 2 0.0 0.0 0.0 1.0 0.0 0.0 0.0
418 SUNSARIPOULTRY SUNSARI 200000000 184300000 15700000 EGG(HEN)56000000NOS.CULLEDLAYERHEN(BYPRODUCT)2... 83 AGRO AND FORESTRY MEDIUM 300 Local - 100%, Foreign - 0% 12 2077 KOSHI 0.066 2 0.0 0.0 0.0 1.0 0.0 0.0 0.0
DataFrame for KOSHI_MINERAL_SMALL: (Empty)

DataFrame for KOSHI_MINERAL_LARGE: (Empty)

DataFrame for KOSHI_MINERAL_MEDIUM: (Empty)

DataFrame for KOSHI_INFRASTRUCTURE_SMALL: (Empty)

DataFrame for KOSHI_INFRASTRUCTURE_LARGE: (Empty)

DataFrame for KOSHI_INFRASTRUCTURE_MEDIUM: (Empty)

DataFrame for GANDAKI_SERVICE_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
269 GA0YUCARGO KASKI 200000000 110000000 90000000 INTERNATIONALCARGOHANDLINGSERVICE88000M.T. 31 SERVICE SMALL 40 Local - 0%, Foreign - 100% 4 2077 GANDAKI 0.166 3 0.0 1.0 0.0 0.0 0.0 0.0 0.0
326 XIELIWANZHONGCONSTRUCTION LAMJUNG 50000000 15000000 35000000 CONSTRUCTIONWORKS(ONVARIOUSSECTORSLIKERAILWAYR... 100 SERVICE SMALL 20 Local - 0%, Foreign - 100% 7 2077 GANDAKI 0.166 3 0.0 1.0 0.0 0.0 0.0 0.0 0.0
DataFrame for GANDAKI_SERVICE_LARGE: (Empty)

DataFrame for GANDAKI_SERVICE_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
33 SAIARCHANAHOSPITAL KASKI 160000000 142265450 17734550 HOSPITAL25BEDS 51 SERVICE MEDIUM 200 Local - 100%, Foreign - 0% 5 2076 GANDAKI 0.166 2 0.0 1.0 0.0 0.0 0.0 0.0 0.0
155 ZH0NGDINGINDUSTRIAL BAGLUNG 200000000 180000000 20000000 CARRYINGOUTFEASIBILITYSTUDYOFCOPPERMINERALS1SITE 27 SERVICE MEDIUM 30 Local - 0%, Foreign - 100% 9 2076 GANDAKI 0.166 2 0.0 1.0 0.0 0.0 0.0 0.0 0.0
DataFrame for GANDAKI_ENERGY_SMALL: (Empty)

DataFrame for GANDAKI_ENERGY_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
25 MOUNTRASUWAHYDROPOWER LAMJUNG 3163000000 3072908562 90091438 \\Q 31 ENERGY LARGE 100 Local - 100%, Foreign - 0% 5 2076 GANDAKI 0.144 1 0.0 1.0 0.0 0.0 0.0 0.0 0.0
26 BARPAKDARAUDIHYDROPOWER GORKHA 1698015666 1650099171 47916495 M10MW 45 ENERGY LARGE 200 Local - 100%, Foreign - 0% 5 2076 GANDAKI 0.144 1 0.0 1.0 0.0 0.0 0.0 0.0 0.0
DataFrame for GANDAKI_ENERGY_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
195 SAURYAVIDHYUTPOWER NAWALPARASI 162650000 160000000 2650000 SOLARELECTRICITY2M.W. 6 ENERGY MEDIUM 50 Local - 100%, Foreign - 0% 11 2076 GANDAKI 0.144 2 0.0 1.0 0.0 0.0 0.0 0.0 0.0
463 HIMALAYANENGINEERINGANDENERGY NAWALPARASI 441264447 433764243 7500204 HYDROELECTRICITY2M.W.ANNUALSALEABLEENERGY\n13.... 28 ENERGY MEDIUM 25 Local - 100%, Foreign - 0% 4 2078 GANDAKI 0.144 2 0.0 1.0 0.0 0.0 0.0 0.0 0.0
DataFrame for GANDAKI_INFORMATION TECHNOLOGY_SMALL: (Empty)

DataFrame for GANDAKI_INFORMATION TECHNOLOGY_LARGE: (Empty)

DataFrame for GANDAKI_INFORMATION TECHNOLOGY_MEDIUM: (Empty)

DataFrame for GANDAKI_TOURISM_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
7 INTENTNEPAL KASKI 50000000 45300000 4700000 HOTEL35BEDSRESTAURANT50SEATS 28 TOURISM SMALL 50 Local - 0%, Foreign - 100% 5 2076 GANDAKI 0.258 3 0.0 1.0 0.0 0.0 0.0 0.0 0.0
8 HONGTEL KASKI 50000000 45200000 4800000 HOTEL35BEDSRESTAURANT50SEATS 28 TOURISM SMALL 50 Local - 0%, Foreign - 100% 5 2076 GANDAKI 0.258 3 0.0 1.0 0.0 0.0 0.0 0.0 0.0
DataFrame for GANDAKI_TOURISM_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
9 SARANGKOTMOUTAINRES0RTANDSPA KASKI 500000000 390700944 109299056 HOTEL48BEDSRESTAURANT200SEATS 60 TOURISM LARGE 200 Local - 100%, Foreign - 0% 5 2076 GANDAKI 0.258 1 0.0 1.0 0.0 0.0 0.0 0.0 0.0
97 BHADRAKALIHOTELIERS KASKI 1250000000 1210000000 40000000 HOTEL140BEDSRESTAURANT200SEATS 100 TOURISM LARGE 1500 Local - 100%, Foreign - 0% 8 2076 GANDAKI 0.258 1 0.0 1.0 0.0 0.0 0.0 0.0 0.0
DataFrame for GANDAKI_TOURISM_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
53 ENTERTRAINMENTS KASKI 249281796 226713796 22568000 ENTERTRAINMENT119588PERSONS 114 TOURISM MEDIUM 1200 Local - 100%, Foreign - 0% 6 2076 GANDAKI 0.258 2 0.0 1.0 0.0 0.0 0.0 0.0 0.0
77 SQUAREROOTHOTEL KASKI 200000000 192000000 8000000 HOTEL48BEDSRESTAURANT80SEATS 40 TOURISM MEDIUM 75 Local - 0%, Foreign - 100% 7 2076 GANDAKI 0.258 2 0.0 1.0 0.0 0.0 0.0 0.0 0.0
DataFrame for GANDAKI_MANUFACTURING_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
41 SHANTIENGINEERING KASKI 16500000 12800000 3700000 METALSTORAGETANKMETALST0REGADHIK0BODYSOLARWATE... 1 MANUFACTURING SMALL 15 Local - 20.29%, Foreign - 79.71% 6 2076 GANDAKI 0.31 3 0.0 1.0 0.0 0.0 0.0 0.0 0.0
362 MANANGBEVERAGES MANANG 93500000 88000000 5500000 WINE125000LTR.CIDER100000LTR. 48 MANUFACTURING SMALL 500 Local - 100%, Foreign - 0% 10 2077 GANDAKI 0.31 3 0.0 1.0 0.0 0.0 0.0 0.0 0.0
DataFrame for GANDAKI_MANUFACTURING_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
479 SIDDHILAXMIFOODS(RICE&DAAL) NAWALPARASI 1130000000 800000000 330000000 RICE33840MTDAL/PULSE(ARHARMASSCHANNAMUGNKERAUM... 224 MANUFACTURING LARGE 2000 Local - 100%, Foreign - 0% 4 2078 GANDAKI 0.31 1 0.0 1.0 0.0 0.0 0.0 0.0 0.0
480 SIDDHILAXMIFOODS(OIL) NAWALPARASI 1730000000 760000000 970000000 RBDPALM/PALMOELINOIL30000MT.REFINEDSOYABEANOIL... 253 MANUFACTURING LARGE 2000 Local - 100%, Foreign - 0% 4 2078 GANDAKI 0.31 1 0.0 1.0 0.0 0.0 0.0 0.0 0.0
DataFrame for GANDAKI_MANUFACTURING_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
116 SIDDHILAXMITUBES NAWALPARASI 249300000 200438000 48862000 MSTUBE/PIPE70000M.T.SSPIPE6000M.T.TELESCOPEPOL... 52 MANUFACTURING MEDIUM 3000 Local - 100%, Foreign - 0% 8 2076 GANDAKI 0.31 2 0.0 1.0 0.0 0.0 0.0 0.0 0.0
140 SAGTANISTEELINDUSTRIES NAWALPARASI 172286684 119061660 53225024 SSSINK50M.T.SSTABLE50M.T.SSEXHAUSTHO0D30M.T.SS... 39 MANUFACTURING MEDIUM 1200 Local - 100%, Foreign - 0% 8 2076 GANDAKI 0.31 2 0.0 1.0 0.0 0.0 0.0 0.0 0.0
DataFrame for GANDAKI_AGRO AND FORESTRY_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
353 JANATAAGR0ANDFEED NAWALPARASI 155000000 107661842 47338158 ANIMALFEED(PELLET/DISC)24000M.T. 45 AGRO AND FORESTRY SMALL 1000 Local - 100%, Foreign - 0% 9 2077 GANDAKI 0.066 3 0.0 1.0 0.0 0.0 0.0 0.0 0.0
472 HELAINGHERBSNEPAL NAWALPARASI 10000000 5140000 4860000 HERBALPOWDER20MT.ESSENTIALOIL14000LITREDRIEDHE... 34 AGRO AND FORESTRY SMALL 100 Local - 40%, Foreign - 60% 4 2078 GANDAKI 0.066 3 0.0 1.0 0.0 0.0 0.0 0.0 0.0
DataFrame for GANDAKI_AGRO AND FORESTRY_LARGE: (Empty)

DataFrame for GANDAKI_AGRO AND FORESTRY_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
37 ANNAPURNARESEARCHCENTERANDFARMING NAWALPARASI 191431250 184931250 6500000 CHICKEN192000KGCHICKS432000NOSEGGS1500000NOSPO... 36 AGRO AND FORESTRY MEDIUM 200 Local - 100%, Foreign - 0% 6 2076 GANDAKI 0.066 2 0.0 1.0 0.0 0.0 0.0 0.0 0.0
190 POKHARABHANJYANGMULTIAGRICULTURE TANAHU 250000000 237000000 13000000 VEGETABLES450M.T.PIG20M.T.FISH20M.T.MILK210KL. 35 AGRO AND FORESTRY MEDIUM 20 Local - 100%, Foreign - 0% 11 2076 GANDAKI 0.066 2 0.0 1.0 0.0 0.0 0.0 0.0 0.0
DataFrame for GANDAKI_MINERAL_SMALL: (Empty)

DataFrame for GANDAKI_MINERAL_LARGE: (Empty)

DataFrame for GANDAKI_MINERAL_MEDIUM: (Empty)

DataFrame for GANDAKI_INFRASTRUCTURE_SMALL: (Empty)

DataFrame for GANDAKI_INFRASTRUCTURE_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
354 JAYABHADRAKALIDEVELOPERS KASKI 1574442000 1557369750 17072250 RESIDENTIALAPARTMENTS267964SQ.FT. 18 INFRASTRUCTURE LARGE 700 Local - 100%, Foreign - 0% 9 2077 GANDAKI 0.004 1 0.0 1.0 0.0 0.0 0.0 0.0 0.0
DataFrame for GANDAKI_INFRASTRUCTURE_MEDIUM: (Empty)

DataFrame for LUMBINI_SERVICE_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
322 RUPANDEHIC.T.ANDDIAGNOSISCENTER RUPANDEHI 45300000 38200000 7100000 C.T.SCANSERVICE15000CASES 17 SERVICE SMALL 100 Local - 100%, Foreign - 0% 7 2077 LUMBINI 0.166 3 0.0 0.0 0.0 0.0 1.0 0.0 0.0
DataFrame for LUMBINI_SERVICE_LARGE: (Empty)

DataFrame for LUMBINI_SERVICE_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
402 ANNAPURNACORPORATION RUPANDEHI 283771900 181675000 102096900 CHAMAL5605M.T.DALL693\nM.T.GEDAGUD1770M.T.MASA... 23 SERVICE MEDIUM 20 Local - 100%, Foreign - 0% 12 2077 LUMBINI 0.166 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0
496 GHORAHICEMENTINDUSTRYLIMITED(CEMENTPACKEGING) BANKE 210338607 167189200 43149407 CEMENT120000MT. 48 SERVICE MEDIUM 1000 Local - 100%, Foreign - 0% 5 2078 LUMBINI 0.166 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0
DataFrame for LUMBINI_ENERGY_SMALL: (Empty)

DataFrame for LUMBINI_ENERGY_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
175 SHANGRILAHYDROPOWER RUKUM 4998000000 4938360000 59640000 àœà2àpà¿à°|à¥Öà°*à¥Öà°21M.W. 57 ENERGY LARGE 50 Local - 100%, Foreign - 0% 10 2076 LUMBINI 0.144 1 0.0 0.0 0.0 0.0 1.0 0.0 0.0
278 POSITIVEENERGY KAPILBASTU 945000000 941000000 4000000 SOLARELECTRICITY10M.W. 10 ENERGY LARGE 5 Local - 100%, Foreign - 0% 5 2077 LUMBINI 0.144 1 0.0 0.0 0.0 0.0 1.0 0.0 0.0
DataFrame for LUMBINI_ENERGY_MEDIUM: (Empty)

DataFrame for LUMBINI_INFORMATION TECHNOLOGY_SMALL: (Empty)

DataFrame for LUMBINI_INFORMATION TECHNOLOGY_LARGE: (Empty)

DataFrame for LUMBINI_INFORMATION TECHNOLOGY_MEDIUM: (Empty)

DataFrame for LUMBINI_TOURISM_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
258 NIRVANALUXURYINTERNATINOAL RUPANDEHI 160000000 110000000 50000000 CASINOPLAYERS(FOREIGNERS0NLY)73000PERSONS 54 TOURISM SMALL 150 Local - 100%, Foreign - 0% 4 2077 LUMBINI 0.258 3 0.0 0.0 0.0 0.0 1.0 0.0 0.0
395 SIDDARTHAGAMINGZONE RUPANDEHI 150000000 101000000 49000000 CASINOPLAYERS/GUESTS(FOREIGNONLY)91250PERSONS 63 TOURISM SMALL 200 Local - 100%, Foreign - 0% 12 2077 LUMBINI 0.258 3 0.0 0.0 0.0 0.0 1.0 0.0 0.0
DataFrame for LUMBINI_TOURISM_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
106 ASIANINTERNATIONALREGENCY RUPANDEHI 2380000000 2340000000 40000000 HOTEL176BEDSRESTAURANT225SEATS 97 TOURISM LARGE 2000 Local - 100%, Foreign - 0% 8 2076 LUMBINI 0.258 1 0.0 0.0 0.0 0.0 1.0 0.0 0.0
202 SIPRABHYAHOTELSANDRES0RTS(1) BARDIYA 1300000000 1250000000 50000000 HOTEL54BEDSRESTAURANT40SEATS 75 TOURISM LARGE 1500 Local - 100%, Foreign - 0% 11 2076 LUMBINI 0.258 1 0.0 0.0 0.0 0.0 1.0 0.0 0.0
DataFrame for LUMBINI_TOURISM_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
425 HOTELMEGASECONDEDITION RUPANDEHI 391798454 358217150 33581304 HOTEL130BEDRESTAURANT180SEAT 135 TOURISM MEDIUM 2000 Local - 100%, Foreign - 0% 1 2078 LUMBINI 0.258 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0
DataFrame for LUMBINI_MANUFACTURING_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
336 DIVYANEPALAYURVEDAPHARMACY RUPANDEHI 125000000 105000000 20000000 VATI/GUGGUL/CAPSULE/TABLET900000KG.SYRUP/ASVA\... 35 MANUFACTURING SMALL 500 Local - 100%, Foreign - 0% 8 2077 LUMBINI 0.31 3 0.0 0.0 0.0 0.0 1.0 0.0 0.0
375 TIRUPATIMETALINDUSTRIES RUPANDEHI 641000000 141000000 500000000 LEADINGOT12000M.T. 111 MANUFACTURING SMALL 250 Local - 100%, Foreign - 0% 11 2077 LUMBINI 0.31 3 0.0 0.0 0.0 0.0 1.0 0.0 0.0
DataFrame for LUMBINI_MANUFACTURING_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
270 MEXOTILES KAPILBASTU 667897700 578852500 89045200 FLOORTILES16000000PIECESWALLTILES12500000PIECES 114 MANUFACTURING LARGE 2000 Local - 100%, Foreign - 0% 4 2077 LUMBINI 0.31 1 0.0 0.0 0.0 0.0 1.0 0.0 0.0
390 TEJSUBEVERAGES RUPANDEHI 1500000000 800000000 700000000 JUICE18000KL.CARBONATEDSOFTDRINK18000KL.ENERGY... 150 MANUFACTURING LARGE 1000 Local - 100%, Foreign - 0% 12 2077 LUMBINI 0.31 1 0.0 0.0 0.0 0.0 1.0 0.0 0.0
DataFrame for LUMBINI_MANUFACTURING_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
23 MODERNDOORS&WOODPRODUCTS BANKE 248846000 155996000 92850000 PLYWOODFALSEDOOR6000000SQUAREMETER\nDECORATEDB... 124 MANUFACTURING MEDIUM 500 Local - 100%, Foreign - 0% 5 2076 LUMBINI 0.31 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0
50 ATALPLYWOODINDUSTRIES KAPILBASTU 233000000 185000000 48000000 PLYWOOD4500000SQ.FT.FALSEDOOR3000000SQ.FT. 87 MANUFACTURING MEDIUM 500 Local - 100%, Foreign - 0% 6 2076 LUMBINI 0.31 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0
DataFrame for LUMBINI_AGRO AND FORESTRY_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
384 ARAMBHAAGR0INDUSTRIES DANG 135000000 110000000 25000000 GINGERSLICE500M.T.GINGERCANDY200M.T.TURMERICSL... 29 AGRO AND FORESTRY SMALL 100 Local - 100%, Foreign - 0% 11 2077 LUMBINI 0.066 3 0.0 0.0 0.0 0.0 1.0 0.0 0.0
DataFrame for LUMBINI_AGRO AND FORESTRY_LARGE: (Empty)

DataFrame for LUMBINI_AGRO AND FORESTRY_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
428 O.C.B.PELLETFEED BANKE 615000000 405000000 210000000 POULTRYFEED80000MT.FISHFEED20000MT.CATTLEFEED2... 83 AGRO AND FORESTRY MEDIUM 2000 Local - 100%, Foreign - 0% 1 2078 LUMBINI 0.066 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0
495 AGRIVASTUCOLDST0RAGE KAPILBASTU 234192207 160104320 74087887 COLDST0RAGE1800MT.PRODUCTIONPROCESSINGAND\nSTO... 27 AGRO AND FORESTRY MEDIUM 300 Local - 100%, Foreign - 0% 5 2078 LUMBINI 0.066 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0
DataFrame for LUMBINI_MINERAL_SMALL: (Empty)

DataFrame for LUMBINI_MINERAL_LARGE: (Empty)

DataFrame for LUMBINI_MINERAL_MEDIUM: (Empty)

DataFrame for LUMBINI_INFRASTRUCTURE_SMALL: (Empty)

DataFrame for LUMBINI_INFRASTRUCTURE_LARGE: (Empty)

DataFrame for LUMBINI_INFRASTRUCTURE_MEDIUM: (Empty)

DataFrame for MADHESH_SERVICE_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
128 ASHWINIENGINEERINGNEPAL SIRAHA 50000000 28950000 21050000 VARIOUSKINDS0FCONSTRUCTIONWORKS(CONSTRUCTIONRE... 68 SERVICE SMALL 10 Local - 0%, Foreign - 100% 8 2076 MADHESH 0.166 3 0.0 0.0 0.0 0.0 0.0 1.0 0.0
DataFrame for MADHESH_SERVICE_LARGE: (Empty)

DataFrame for MADHESH_SERVICE_MEDIUM: (Empty)

DataFrame for MADHESH_ENERGY_SMALL: (Empty)

DataFrame for MADHESH_ENERGY_LARGE: (Empty)

DataFrame for MADHESH_ENERGY_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
206 SAGARMATHAENERTY&CONSTRUCTION DHANUSHA 296000000 290000000 6000000 SOLAR(PV)3M.W. 18 ENERGY MEDIUM 10 Local - 100%, Foreign - 0% 11 2076 MADHESH 0.144 2 0.0 0.0 0.0 0.0 0.0 1.0 0.0
DataFrame for MADHESH_INFORMATION TECHNOLOGY_SMALL: (Empty)

DataFrame for MADHESH_INFORMATION TECHNOLOGY_LARGE: (Empty)

DataFrame for MADHESH_INFORMATION TECHNOLOGY_MEDIUM: (Empty)

DataFrame for MADHESH_TOURISM_SMALL: (Empty)

DataFrame for MADHESH_TOURISM_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
75 HOTELICHCHHA BARA 559000000 534000000 25000000 HOTEL96BEDSRESTAURANT200SEATS 100 TOURISM LARGE 400 Local - 100%, Foreign - 0% 7 2076 MADHESH 0.258 1 0.0 0.0 0.0 0.0 0.0 1.0 0.0
DataFrame for MADHESH_TOURISM_MEDIUM: (Empty)

DataFrame for MADHESH_MANUFACTURING_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
74 RANGPAL BARA 10000000 5500000 4500000 INCENSESTICKS7000KGVERMILLION(SINDO0R)700\nKGT... 28 MANUFACTURING SMALL 25 Local - 0%, Foreign - 100% 7 2076 MADHESH 0.31 3 0.0 0.0 0.0 0.0 0.0 1.0 0.0
185 ANNAPURNALEATHERTANNINGINDUSTRY BARA 100000000 57000000 43000000 WET-BLUE:600000SQ.FT.CRUSTLEATHER:375000SQ.FT.... 38 MANUFACTURING SMALL 200 Local - 0%, Foreign - 100% 10 2076 MADHESH 0.31 3 0.0 0.0 0.0 0.0 0.0 1.0 0.0
DataFrame for MADHESH_MANUFACTURING_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
45 HIMALASIMSTEEL PARSA 4944089000 2472400000 2471689000 GIWIRE25000M.T.MSANGLE/CHANNEL/BEAM50000M.T.MS... 550 MANUFACTURING LARGE 75000 Local - 100%, Foreign - 0% 6 2076 MADHESH 0.31 1 0.0 0.0 0.0 0.0 0.0 1.0 0.0
227 NEPALCERAMICINDUSTRY BARA 662000000 595732240 66267760 FL0ORTILES28266000SQ.FT. 200 MANUFACTURING LARGE 500 Local - 100%, Foreign - 0% 2 2077 MADHESH 0.31 1 0.0 0.0 0.0 0.0 0.0 1.0 0.0
DataFrame for MADHESH_MANUFACTURING_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
34 PRESTIGETEXTILEINDUSTRIES PARSA 248112790 148062534 100050256 WOVENFABRIC2350000METERKNITTEDFABRIC135TON 71 MANUFACTURING MEDIUM 500 Local - 100%, Foreign - 0% 5 2076 MADHESH 0.31 2 0.0 0.0 0.0 0.0 0.0 1.0 0.0
47 VIJAYROSHANSTEELINDUSTRIES PARSA 223500000 143500000 80000000 SSDINNERANDLAUNCHPLATE2000M.T.SSBOULANDMUG\n80... 45 MANUFACTURING MEDIUM 1000 Local - 100%, Foreign - 0% 6 2076 MADHESH 0.31 2 0.0 0.0 0.0 0.0 0.0 1.0 0.0
DataFrame for MADHESH_AGRO AND FORESTRY_SMALL: (Empty)

DataFrame for MADHESH_AGRO AND FORESTRY_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
284 JANAKPURAGR0FARM DHANUSHA 1042000000 900000000 142000000 EGGS129405097PIECESRETIREDBIRDS250000K.G.\n0RG... 350 AGRO AND FORESTRY LARGE 1000 Local - 100%, Foreign - 0% 5 2077 MADHESH 0.066 1 0.0 0.0 0.0 0.0 0.0 1.0 0.0
DataFrame for MADHESH_AGRO AND FORESTRY_MEDIUM: (Empty)

DataFrame for MADHESH_MINERAL_SMALL: (Empty)

DataFrame for MADHESH_MINERAL_LARGE: (Empty)

DataFrame for MADHESH_MINERAL_MEDIUM: (Empty)

DataFrame for MADHESH_INFRASTRUCTURE_SMALL: (Empty)

DataFrame for MADHESH_INFRASTRUCTURE_LARGE: (Empty)

DataFrame for MADHESH_INFRASTRUCTURE_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
408 EMPERORDEVELOPERS MAHOTTARI 163873000 155873000 8000000 RESIDENTIALAPARTMENTS100000SQ.FT. 27 INFRASTRUCTURE MEDIUM 500 Local - 100%, Foreign - 0% 12 2077 MADHESH 0.004 2 0.0 0.0 0.0 0.0 0.0 1.0 0.0
DataFrame for KARNALI_SERVICE_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
256 PRISTINENEPALTERMINALS KALIKOT 80000000 64000000 16000000 CONTAINERS14550MTBULK&BREAKBULK9050MT 74 SERVICE SMALL 100 Local - 36%, Foreign - 64% 4 2077 KARNALI 0.166 3 0.0 0.0 1.0 0.0 0.0 0.0 0.0
DataFrame for KARNALI_SERVICE_LARGE: (Empty)

DataFrame for KARNALI_SERVICE_MEDIUM: (Empty)

DataFrame for KARNALI_ENERGY_SMALL: (Empty)

DataFrame for KARNALI_ENERGY_LARGE:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
309 SETIKHOLAHYDROPWER KALIKOT 5005648000 4945816000 59832000 HYDROELECTRICITY22M.W. 36 ENERGY LARGE 100 Local - 100%, Foreign - 0% 6 2077 KARNALI 0.144 1 0.0 0.0 1.0 0.0 0.0 0.0 0.0
432 MANDAKINIHYDROPOWERCOMPANYLIMITED-1 JAJARKOT 569981000 565307297 4673703 HYDROELECTRICITY2.9M.W. 40 ENERGY LARGE 20 Local - 100%, Foreign - 0% 1 2078 KARNALI 0.144 1 0.0 0.0 1.0 0.0 0.0 0.0 0.0
DataFrame for KARNALI_ENERGY_MEDIUM: (Empty)

DataFrame for KARNALI_INFORMATION TECHNOLOGY_SMALL: (Empty)

DataFrame for KARNALI_INFORMATION TECHNOLOGY_LARGE: (Empty)

DataFrame for KARNALI_INFORMATION TECHNOLOGY_MEDIUM: (Empty)

DataFrame for KARNALI_TOURISM_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
117 SPRINGHILLHOTEL HUMLA 50000000 47000000 3000000 HOTEL22BEDSRESTAURANT40SEATS 29 TOURISM SMALL 60 Local - 0%, Foreign - 100% 8 2076 KARNALI 0.258 3 0.0 0.0 1.0 0.0 0.0 0.0 0.0
285 SAMADHIVILLAGE SALYAN 50000000 37500000 12500000 HOTEL20BEDSRESTAURANT100SEATS 17 TOURISM SMALL 50 Local - 0%, Foreign - 100% 6 2077 KARNALI 0.258 3 0.0 0.0 1.0 0.0 0.0 0.0 0.0
DataFrame for KARNALI_TOURISM_LARGE: (Empty)

DataFrame for KARNALI_TOURISM_MEDIUM: (Empty)

DataFrame for KARNALI_MANUFACTURING_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
250 NEBULAENERGY HUMLA 150000000 105000000 45000000 ELECTRICVEHICLESASSEMBLING300NOS. 67 MANUFACTURING SMALL 500 Local - 100%, Foreign - 0% 3 2077 KARNALI 0.31 3 0.0 0.0 1.0 0.0 0.0 0.0 0.0
434 ADIRATEXTILEINDUSTRIES KALIKOT 200000000 137100000 62900000 VARIOUSFABRICS4200000SQ.M 59 MANUFACTURING SMALL 800 Local - 100%, Foreign - 0% 1 2078 KARNALI 0.31 3 0.0 0.0 1.0 0.0 0.0 0.0 0.0
DataFrame for KARNALI_MANUFACTURING_LARGE: (Empty)

DataFrame for KARNALI_MANUFACTURING_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
249 HIMALAYANRENEWABLEOILINDUSTRY DOLPA 250000000 224492402 25507598 PETROL-1485KLDIESEL-1485KLLUBRICANTS(BYPRODUCT... 42 MANUFACTURING MEDIUM 2000 Local - 20%, Foreign - 80% 3 2077 KARNALI 0.31 2 0.0 0.0 1.0 0.0 0.0 0.0 0.0
342 PATHIVARAMATAFERTILIZERINDUSTRIES JAJARKOT 219800000 162400000 57400000 CHEMICALFERTILIZER3000M.T. 45 MANUFACTURING MEDIUM 900 Local - 100%, Foreign - 0% 9 2077 KARNALI 0.31 2 0.0 0.0 1.0 0.0 0.0 0.0 0.0
DataFrame for KARNALI_AGRO AND FORESTRY_SMALL: (Empty)

DataFrame for KARNALI_AGRO AND FORESTRY_LARGE: (Empty)

DataFrame for KARNALI_AGRO AND FORESTRY_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
351 BOLBOMFEEDINDUSTRIES KALIKOT 340000000 213000000 127000000 ANIMALFEED(PELLET/DISC)73000M.T. 55 AGRO AND FORESTRY MEDIUM 1000 Local - 100%, Foreign - 0% 9 2077 KARNALI 0.066 2 0.0 0.0 1.0 0.0 0.0 0.0 0.0
DataFrame for KARNALI_MINERAL_SMALL: (Empty)

DataFrame for KARNALI_MINERAL_LARGE: (Empty)

DataFrame for KARNALI_MINERAL_MEDIUM: (Empty)

DataFrame for KARNALI_INFRASTRUCTURE_SMALL: (Empty)

DataFrame for KARNALI_INFRASTRUCTURE_LARGE: (Empty)

DataFrame for KARNALI_INFRASTRUCTURE_MEDIUM: (Empty)

DataFrame for SUDUR-PASCHIM_SERVICE_SMALL: (Empty)

DataFrame for SUDUR-PASCHIM_SERVICE_LARGE: (Empty)

DataFrame for SUDUR-PASCHIM_SERVICE_MEDIUM: (Empty)

DataFrame for SUDUR-PASCHIM_ENERGY_SMALL: (Empty)

DataFrame for SUDUR-PASCHIM_ENERGY_LARGE: (Empty)

DataFrame for SUDUR-PASCHIM_ENERGY_MEDIUM: (Empty)

DataFrame for SUDUR-PASCHIM_INFORMATION TECHNOLOGY_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
176 TALKSUREINC. KAILALI 50000000 44000000 6000000 SOFTWAREDEVELOPMENT300PACKAGES 45 INFORMATION TECHNOLOGY SMALL 100 Local - 0%, Foreign - 100% 10 2076 SUDUR-PASCHIM 0.05 3 0.0 0.0 0.0 0.0 0.0 0.0 1.0
DataFrame for SUDUR-PASCHIM_INFORMATION TECHNOLOGY_LARGE: (Empty)

DataFrame for SUDUR-PASCHIM_INFORMATION TECHNOLOGY_MEDIUM: (Empty)

DataFrame for SUDUR-PASCHIM_TOURISM_SMALL: (Empty)

DataFrame for SUDUR-PASCHIM_TOURISM_LARGE: (Empty)

DataFrame for SUDUR-PASCHIM_TOURISM_MEDIUM: (Empty)

DataFrame for SUDUR-PASCHIM_MANUFACTURING_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
323 KAILALIWIREANDSTEELINDUSTRIES KAILALI 150000000 105000000 45000000 GIWIRE6000M.T.GABIONMATTRESS6000M.T. 70 MANUFACTURING SMALL 2000 Local - 100%, Foreign - 0% 7 2077 SUDUR-PASCHIM 0.31 3 0.0 0.0 0.0 0.0 0.0 0.0 1.0
368 SHIVASHAKTIBALUAPRASODHANKENDRA KAILALI 16424900 11474900 4950000 PROCESSEDSAND11000M.T. 18 MANUFACTURING SMALL 100 Local - 100%, Foreign - 0% 10 2077 SUDUR-PASCHIM 0.31 3 0.0 0.0 0.0 0.0 0.0 0.0 1.0
DataFrame for SUDUR-PASCHIM_MANUFACTURING_LARGE: (Empty)

DataFrame for SUDUR-PASCHIM_MANUFACTURING_MEDIUM:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
436 SHEETALINDUSTRIES KAILALI 311540500 261443000 50097500 OXYGENLIQUID2742857CU.M.OXYGENGAS1097143CU.M.N... 40 MANUFACTURING MEDIUM 1500 Local - 100%, Foreign - 0% 1 2078 SUDUR-PASCHIM 0.31 2 0.0 0.0 0.0 0.0 0.0 0.0 1.0
482 MALIKASTEELS KAILALI 411067075 254882075 156185000 MSRIBBEDBAR30000MTGIWIRE8000MT 285 MANUFACTURING MEDIUM 2000 Local - 100%, Foreign - 0% 4 2078 SUDUR-PASCHIM 0.31 2 0.0 0.0 0.0 0.0 0.0 0.0 1.0
DataFrame for SUDUR-PASCHIM_AGRO AND FORESTRY_SMALL:
INDUSTRY NAME DISTRICT TOTAL CAPITAL FIXED CAPITAL WORKING CAPITAL PRODUCT AND ANNUAL CAPACITY EMPLOYMENT CATEGORY SCALE POWER % OF INVESTMENT MONTH YEAR PROVINCE CATEGORY_FREQ SCALE_encode PROVINCE_BAGMATI PROVINCE_GANDAKI PROVINCE_KARNALI PROVINCE_KOSHI PROVINCE_LUMBINI PROVINCE_MADHESH PROVINCE_SUDUR-PASCHIM
341 SHOVAAGR0ANDRESEARCHCENTER BAJURA 150000000 145000000 5000000 APPLE600M.T.WALNUT200M.T.HERBS(BOJHOGURJOETC.)... 264 AGRO AND FORESTRY SMALL 200 Local - 100%, Foreign - 0% 8 2077 SUDUR-PASCHIM 0.066 3 0.0 0.0 0.0 0.0 0.0 0.0 1.0
DataFrame for SUDUR-PASCHIM_AGRO AND FORESTRY_LARGE: (Empty)

DataFrame for SUDUR-PASCHIM_AGRO AND FORESTRY_MEDIUM: (Empty)

DataFrame for SUDUR-PASCHIM_MINERAL_SMALL: (Empty)

DataFrame for SUDUR-PASCHIM_MINERAL_LARGE: (Empty)

DataFrame for SUDUR-PASCHIM_MINERAL_MEDIUM: (Empty)

DataFrame for SUDUR-PASCHIM_INFRASTRUCTURE_SMALL: (Empty)

DataFrame for SUDUR-PASCHIM_INFRASTRUCTURE_LARGE: (Empty)

DataFrame for SUDUR-PASCHIM_INFRASTRUCTURE_MEDIUM: (Empty)
In [105]:
# @title make visualization based on employment

fig = px.violin(df0, y="EMPLOYMENT", x="CATEGORY", color="CATEGORY", box=True, points="all",
                title="Violin Plot of Employment by Category")
fig.show()
In [106]:
# @title Employment Generation per Province:
province_summary = df0.groupby(['PROVINCE']).agg({'EMPLOYMENT': 'sum'})
province_summary = province_summary.sort_values(by='EMPLOYMENT', ascending=False)

display(province_summary)

fig = px.bar(province_summary, x=province_summary.index, y='EMPLOYMENT',
             title='Employment by province')
fig.show()
EMPLOYMENT
PROVINCE
BAGMATI 16700
KOSHI 3761
MADHESH 3537
LUMBINI 3176
GANDAKI 3157
SUDUR-PASCHIM 977
KARNALI 651
In [107]:
# @title  Combined Analysis (using multiple aggregation methods)

combined_analysis = df0.groupby(['CATEGORY', 'PROVINCE']).agg(
    total_capital_invested=('TOTAL CAPITAL', 'sum'),
    avg_employment=('EMPLOYMENT', 'mean')
)
# Display the resulting DataFrame
display(combined_analysis)
total_capital_invested avg_employment
CATEGORY PROVINCE
AGRO AND FORESTRY BAGMATI 1445000000 52.076923
GANDAKI 1557346614 57.142857
KARNALI 340000000 55.000000
KOSHI 1515642000 79.714286
LUMBINI 984192207 46.333333
MADHESH 1042000000 350.000000
SUDUR-PASCHIM 150000000 264.000000
ENERGY BAGMATI 53992062524 67.294118
GANDAKI 57205948565 33.400000
KARNALI 7475629000 35.666667
KOSHI 79389736445 47.333333
LUMBINI 5943000000 33.500000
MADHESH 296000000 18.000000
INFORMATION TECHNOLOGY BAGMATI 3790140358 96.333333
SUDUR-PASCHIM 50000000 45.000000
INFRASTRUCTURE GANDAKI 1574442000 18.000000
MADHESH 163873000 27.000000
MANUFACTURING BAGMATI 10318253800 69.708333
GANDAKI 5807849694 72.076923
KARNALI 1719800000 55.166667
KOSHI 7204402915 61.272727
LUMBINI 12911175152 97.227273
MADHESH 17262109248 82.611111
SUDUR-PASCHIM 1613977012 83.500000
MINERAL BAGMATI 123446032 92.000000
SERVICE BAGMATI 12589005914 72.563380
GANDAKI 728400000 46.600000
KARNALI 80000000 74.000000
KOSHI 665600000 54.000000
LUMBINI 539410507 29.333333
MADHESH 50000000 68.000000
TOURISM BAGMATI 20349736834 42.763441
GANDAKI 4943781796 45.875000
KARNALI 153000000 28.000000
KOSHI 1928091500 76.375000
LUMBINI 6239083454 92.875000
MADHESH 559000000 100.000000
In [108]:
# @title  Combined Analysis (using multiple aggregation methods)

combined_analysis = df0.groupby(['CATEGORY', 'PROVINCE']).agg(
    avg_employment=('EMPLOYMENT', 'mean')
).reset_index()  # Reset index to have 'CATEGORY' and 'PROVINCE' as columns

# Set 'CATEGORY' and 'PROVINCE' as index
combined_analysis = combined_analysis.set_index(['CATEGORY', 'PROVINCE'])

# Display the resulting DataFrame
display(combined_analysis)
avg_employment
CATEGORY PROVINCE
AGRO AND FORESTRY BAGMATI 52.076923
GANDAKI 57.142857
KARNALI 55.000000
KOSHI 79.714286
LUMBINI 46.333333
MADHESH 350.000000
SUDUR-PASCHIM 264.000000
ENERGY BAGMATI 67.294118
GANDAKI 33.400000
KARNALI 35.666667
KOSHI 47.333333
LUMBINI 33.500000
MADHESH 18.000000
INFORMATION TECHNOLOGY BAGMATI 96.333333
SUDUR-PASCHIM 45.000000
INFRASTRUCTURE GANDAKI 18.000000
MADHESH 27.000000
MANUFACTURING BAGMATI 69.708333
GANDAKI 72.076923
KARNALI 55.166667
KOSHI 61.272727
LUMBINI 97.227273
MADHESH 82.611111
SUDUR-PASCHIM 83.500000
MINERAL BAGMATI 92.000000
SERVICE BAGMATI 72.563380
GANDAKI 46.600000
KARNALI 74.000000
KOSHI 54.000000
LUMBINI 29.333333
MADHESH 68.000000
TOURISM BAGMATI 42.763441
GANDAKI 45.875000
KARNALI 28.000000
KOSHI 76.375000
LUMBINI 92.875000
MADHESH 100.000000
In [109]:
# @title  Average Employment by Category and Province

combined_analysis = df0.groupby(['CATEGORY', 'PROVINCE']).agg(
    avg_employment=('EMPLOYMENT', 'mean')
).reset_index()

display(combined_analysis)

fig = px.bar(combined_analysis,
             x="CATEGORY",
             y="avg_employment",
             color="PROVINCE",
             title="Average Employment by Category and Province",
             barmode='group',
             text='avg_employment')

fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.show()
CATEGORY PROVINCE avg_employment
0 AGRO AND FORESTRY BAGMATI 52.076923
1 AGRO AND FORESTRY GANDAKI 57.142857
2 AGRO AND FORESTRY KARNALI 55.000000
3 AGRO AND FORESTRY KOSHI 79.714286
4 AGRO AND FORESTRY LUMBINI 46.333333
5 AGRO AND FORESTRY MADHESH 350.000000
6 AGRO AND FORESTRY SUDUR-PASCHIM 264.000000
7 ENERGY BAGMATI 67.294118
8 ENERGY GANDAKI 33.400000
9 ENERGY KARNALI 35.666667
10 ENERGY KOSHI 47.333333
11 ENERGY LUMBINI 33.500000
12 ENERGY MADHESH 18.000000
13 INFORMATION TECHNOLOGY BAGMATI 96.333333
14 INFORMATION TECHNOLOGY SUDUR-PASCHIM 45.000000
15 INFRASTRUCTURE GANDAKI 18.000000
16 INFRASTRUCTURE MADHESH 27.000000
17 MANUFACTURING BAGMATI 69.708333
18 MANUFACTURING GANDAKI 72.076923
19 MANUFACTURING KARNALI 55.166667
20 MANUFACTURING KOSHI 61.272727
21 MANUFACTURING LUMBINI 97.227273
22 MANUFACTURING MADHESH 82.611111
23 MANUFACTURING SUDUR-PASCHIM 83.500000
24 MINERAL BAGMATI 92.000000
25 SERVICE BAGMATI 72.563380
26 SERVICE GANDAKI 46.600000
27 SERVICE KARNALI 74.000000
28 SERVICE KOSHI 54.000000
29 SERVICE LUMBINI 29.333333
30 SERVICE MADHESH 68.000000
31 TOURISM BAGMATI 42.763441
32 TOURISM GANDAKI 45.875000
33 TOURISM KARNALI 28.000000
34 TOURISM KOSHI 76.375000
35 TOURISM LUMBINI 92.875000
36 TOURISM MADHESH 100.000000
In [110]:
# @title a bar chart showing total capital invested by category
combined_analysis = df0.groupby(['CATEGORY', 'PROVINCE']).agg(
    total_capital_invested=('TOTAL CAPITAL', 'sum'),
    avg_employment=('EMPLOYMENT', 'mean')
).reset_index()

fig = px.bar(combined_analysis, x='CATEGORY', y='total_capital_invested', color='PROVINCE',
             title='Total Capital Invested by Category and Province',
             labels={'total_capital_invested': 'Total Capital Invested', 'CATEGORY': 'Category', 'PROVINCE': 'Province'})
fig.show()
In [111]:
# @title scatter plot showing the relationship between total capital invested and average employment
fig = px.scatter(combined_analysis, x='total_capital_invested', y='avg_employment', color='CATEGORY',
                 title='Relationship between Total Capital Invested and Average Employment',
                 labels={'total_capital_invested': 'Total Capital Invested', 'avg_employment': 'Average Employment', 'CATEGORY': 'Category'},
                 hover_data=['PROVINCE']) # show province when hovering over data points
fig.show()
In [112]:
def create_employment_diagram(df):

  # group data by province and district, summing employment
  employment_by_district_province = df.groupby(['PROVINCE', 'DISTRICT'])['EMPLOYMENT'].sum().reset_index()

  # create the stacked bar chart
  fig = go.Figure()

  for province in employment_by_district_province['PROVINCE'].unique():
      province_data = employment_by_district_province[employment_by_district_province['PROVINCE'] == province]
      fig.add_trace(go.Bar(
          x=province_data['DISTRICT'],
          y=province_data['EMPLOYMENT'],
          name=province,
          text=province_data['EMPLOYMENT'],  # display employment values on bars
          textposition='auto'
      ))

  fig.update_layout(
      barmode='stack',  # stack bars for each province
      title='Employment by District and Province',
      xaxis_title='District',
      yaxis_title='Employment',
      xaxis={'categoryorder':'total descending'}, # Order districts by total employment
      width=1000,  # adjust width
      height=600, # adjust height
      legend_title='Province',
  )

  fig.update_traces(texttemplate='%{text:.2s}', textposition='outside') #show employment values outside the bar
  return fig

fig = create_employment_diagram(df0)
fig.show()
In [113]:
def create_capital_diagram(df):
    # group data by province, category, and scale, summing total capital
    capital_data = df.groupby(['PROVINCE', 'CATEGORY', 'SCALE'])['TOTAL CAPITAL'].sum().reset_index()

    display(capital_data)

    # create subplots
    num_provinces = len(capital_data['PROVINCE'].unique())
    rows = (num_provinces + 2) // 3  # calculate rows needed, ensuring at least 1 row
    cols = min(num_provinces, 3)       # limit columns to 3
    fig = make_subplots(rows=rows, cols=cols, subplot_titles=capital_data['PROVINCE'].unique(), shared_xaxes=False)

    subplot_index = 1

    for province in capital_data['PROVINCE'].unique():
        province_data = capital_data[capital_data['PROVINCE'] == province]
        row = (subplot_index - 1) // cols + 1
        col = (subplot_index - 1) % cols + 1

        for category in province_data['CATEGORY'].unique():
            category_data = province_data[province_data['CATEGORY'] == category]
            fig.add_trace(go.Bar(
                x=category_data['SCALE'],
                y=category_data['TOTAL CAPITAL'],
                name=category,
                text=category_data['TOTAL CAPITAL'],
                textposition='auto'
            ), row=row, col=col)

        subplot_index += 1

    fig.update_layout(
        barmode='stack',
        title='Total Capital by Province, Category, and Scale',
        width=1200,
        height=800,
        showlegend=True,
        title_x=0.5, # center the title
        margin=dict(l=50, r=50, t=100, b=50) # adjust margins for better spacing
    )

    fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
    fig.update_xaxes(tickangle=45, tickfont=dict(size=10)) # rotate x-axis labels for better visibility
    return fig

fig = create_capital_diagram(df0)
fig.show()
PROVINCE CATEGORY SCALE TOTAL CAPITAL
0 BAGMATI AGRO AND FORESTRY MEDIUM 710000000
1 BAGMATI AGRO AND FORESTRY SMALL 735000000
2 BAGMATI ENERGY LARGE 53148711875
3 BAGMATI ENERGY MEDIUM 725850649
4 BAGMATI ENERGY SMALL 117500000
... ... ... ... ...
67 MADHESH TOURISM LARGE 559000000
68 SUDUR-PASCHIM AGRO AND FORESTRY SMALL 150000000
69 SUDUR-PASCHIM INFORMATION TECHNOLOGY SMALL 50000000
70 SUDUR-PASCHIM MANUFACTURING MEDIUM 959207641
71 SUDUR-PASCHIM MANUFACTURING SMALL 654769371

72 rows × 4 columns